From 31a87dd8a802f2ebfd41c9ddbd46cf5055ce2b91 Mon Sep 17 00:00:00 2001 From: Chongshan0Lin Date: Sun, 9 Nov 2025 11:04:19 -0600 Subject: [PATCH 01/11] Initialize table in __init__ method of each node/edge class --- .gitignore | 5 +- .../sql_database/sql_arxiv_authors.py | 1 + .../sql_database/sql_arxiv_categories.py | 1 + .../sql_database/sql_arxiv_citations.py | 1 + .../sql_database/sql_arxiv_figures.py | 1 + .../sql_database/sql_arxiv_paper_authors.py | 1 + .../sql_arxiv_paper_categories.py | 1 + .../sql_database/sql_arxiv_paper_figures.py | 1 + .../sql_database/sql_arxiv_paper_tables.py | 3 +- .../sql_database/sql_arxiv_papers.py | 1 + .../sql_arxiv_paragraph_references.py | 1 + .../sql_database/sql_arxiv_paragraphs.py | 1 + .../sql_database/sql_arxiv_sections.py | 1 + .../sql_database/sql_arxiv_tables.py | 1 + ...rcade_complete_tutorial_with_imports.ipynb | 250 +++++++++--------- 15 files changed, 149 insertions(+), 121 deletions(-) diff --git a/.gitignore b/.gitignore index 36c9109..6bb2ddd 100644 --- a/.gitignore +++ b/.gitignore @@ -178,4 +178,7 @@ paragraph_generation_models/ paragraph_generation_models/* token_embedding_cache/ wandb/ -*.json \ No newline at end of file +*.json + +helper/ +helper/*.py \ No newline at end of file diff --git a/research_arcade/sql_database/sql_arxiv_authors.py b/research_arcade/sql_database/sql_arxiv_authors.py index 1239e1b..0ceb8e7 100644 --- a/research_arcade/sql_database/sql_arxiv_authors.py +++ b/research_arcade/sql_database/sql_arxiv_authors.py @@ -20,6 +20,7 @@ def __init__(self, host: str, dbname: str, user: str, password: str, port: str): self.password = password self.port = port self.autocommit = True + self.create_authors_table() def _get_connection(self): conn = psycopg2.connect( diff --git a/research_arcade/sql_database/sql_arxiv_categories.py b/research_arcade/sql_database/sql_arxiv_categories.py index 15c4032..c3c88b0 100644 --- a/research_arcade/sql_database/sql_arxiv_categories.py +++ b/research_arcade/sql_database/sql_arxiv_categories.py @@ -21,6 +21,7 @@ def __init__(self, host: str, dbname: str, user: str, password: str, port: str): self.password = password self.port = port self.autocommit = True + self.create_categories_table() def _get_connection(self): conn = psycopg2.connect( diff --git a/research_arcade/sql_database/sql_arxiv_citations.py b/research_arcade/sql_database/sql_arxiv_citations.py index cad96c1..6fb62e5 100644 --- a/research_arcade/sql_database/sql_arxiv_citations.py +++ b/research_arcade/sql_database/sql_arxiv_citations.py @@ -22,6 +22,7 @@ def __init__(self, host: str, dbname: str, user: str, password: str, port: str): self.password = password self.port = port self.autocommit = True + self.create_citations_table() def _get_connection(self): conn = psycopg2.connect( diff --git a/research_arcade/sql_database/sql_arxiv_figures.py b/research_arcade/sql_database/sql_arxiv_figures.py index 9fe19fb..e3d971c 100644 --- a/research_arcade/sql_database/sql_arxiv_figures.py +++ b/research_arcade/sql_database/sql_arxiv_figures.py @@ -22,6 +22,7 @@ def __init__(self, host: str, dbname: str, user: str, password: str, port: str): self.password = password self.port = port self.autocommit = True + self.create_figures_table() def _get_connection(self): conn = psycopg2.connect( diff --git a/research_arcade/sql_database/sql_arxiv_paper_authors.py b/research_arcade/sql_database/sql_arxiv_paper_authors.py index 50de38f..62f5188 100644 --- a/research_arcade/sql_database/sql_arxiv_paper_authors.py +++ b/research_arcade/sql_database/sql_arxiv_paper_authors.py @@ -19,6 +19,7 @@ def __init__(self, host: str, dbname: str, user: str, password: str, port: str): self.password = password self.port = port self.autocommit = True + self.create_paper_authors_table() def _get_connection(self): conn = psycopg2.connect( diff --git a/research_arcade/sql_database/sql_arxiv_paper_categories.py b/research_arcade/sql_database/sql_arxiv_paper_categories.py index 33b1bac..cad7e0b 100644 --- a/research_arcade/sql_database/sql_arxiv_paper_categories.py +++ b/research_arcade/sql_database/sql_arxiv_paper_categories.py @@ -19,6 +19,7 @@ def __init__(self, host: str, dbname: str, user: str, password: str, port: str): self.password = password self.port = port self.autocommit = True + self.create_paper_category_table() def _get_connection(self): conn = psycopg2.connect( diff --git a/research_arcade/sql_database/sql_arxiv_paper_figures.py b/research_arcade/sql_database/sql_arxiv_paper_figures.py index 2fd5388..cf9a510 100644 --- a/research_arcade/sql_database/sql_arxiv_paper_figures.py +++ b/research_arcade/sql_database/sql_arxiv_paper_figures.py @@ -19,6 +19,7 @@ def __init__(self, host: str, dbname: str, user: str, password: str, port: str): self.password = password self.port = port self.autocommit = True + self.create_paper_figures_table() def _get_connection(self): conn = psycopg2.connect( diff --git a/research_arcade/sql_database/sql_arxiv_paper_tables.py b/research_arcade/sql_database/sql_arxiv_paper_tables.py index 0deeaec..7163b4e 100644 --- a/research_arcade/sql_database/sql_arxiv_paper_tables.py +++ b/research_arcade/sql_database/sql_arxiv_paper_tables.py @@ -12,8 +12,9 @@ def __init__(self, host: str, dbname: str, user: str, password: str, port: str): self.dbname = dbname self.user = user self.password = password - self.autocommit = port + self.port = port self.autocommit = True + self.create_paper_tables_table() def _get_connection(self): conn = psycopg2.connect( diff --git a/research_arcade/sql_database/sql_arxiv_papers.py b/research_arcade/sql_database/sql_arxiv_papers.py index 0943296..bce00f4 100644 --- a/research_arcade/sql_database/sql_arxiv_papers.py +++ b/research_arcade/sql_database/sql_arxiv_papers.py @@ -20,6 +20,7 @@ def __init__(self, host: str, dbname: str, user: str, password: str, port: str): self.password = password self.port = port self.autocommit = True + self.create_papers_table() def _get_connection(self): conn = psycopg2.connect( diff --git a/research_arcade/sql_database/sql_arxiv_paragraph_references.py b/research_arcade/sql_database/sql_arxiv_paragraph_references.py index 0ee3530..7ad084b 100644 --- a/research_arcade/sql_database/sql_arxiv_paragraph_references.py +++ b/research_arcade/sql_database/sql_arxiv_paragraph_references.py @@ -20,6 +20,7 @@ def __init__(self, host: str, dbname: str, user: str, password: str, port: str): self.password = password self.port = port self.autocommit = True + self.create_paragraph_references_table() def _get_connection(self): conn = psycopg2.connect( diff --git a/research_arcade/sql_database/sql_arxiv_paragraphs.py b/research_arcade/sql_database/sql_arxiv_paragraphs.py index 24c924e..3477453 100644 --- a/research_arcade/sql_database/sql_arxiv_paragraphs.py +++ b/research_arcade/sql_database/sql_arxiv_paragraphs.py @@ -22,6 +22,7 @@ def __init__(self, host: str, dbname: str, user: str, password: str, port: str): self.password = password self.port = port self.autocommit = True + self.create_paragraphs_table() def _get_connection(self): conn = psycopg2.connect( diff --git a/research_arcade/sql_database/sql_arxiv_sections.py b/research_arcade/sql_database/sql_arxiv_sections.py index 81f8b5c..582350f 100644 --- a/research_arcade/sql_database/sql_arxiv_sections.py +++ b/research_arcade/sql_database/sql_arxiv_sections.py @@ -19,6 +19,7 @@ def __init__(self, host: str, dbname: str, user: str, password: str, port: str): self.password = password self.port = port self.autocommit = True + self.create_sections_table() def _get_connection(self): conn = psycopg2.connect( diff --git a/research_arcade/sql_database/sql_arxiv_tables.py b/research_arcade/sql_database/sql_arxiv_tables.py index 6c64f34..89eb431 100644 --- a/research_arcade/sql_database/sql_arxiv_tables.py +++ b/research_arcade/sql_database/sql_arxiv_tables.py @@ -19,6 +19,7 @@ def __init__(self, host: str, dbname: str, user: str, password: str, port: str): self.password = password self.port = port self.autocommit = True + self.create_tables_table() def _get_connection(self): conn = psycopg2.connect( diff --git a/research_arcade_complete_tutorial_with_imports.ipynb b/research_arcade_complete_tutorial_with_imports.ipynb index d27e125..f7452f8 100644 --- a/research_arcade_complete_tutorial_with_imports.ipynb +++ b/research_arcade_complete_tutorial_with_imports.ipynb @@ -71,9 +71,36 @@ "metadata": {}, "outputs": [], "source": [ - "db_type = \"csv\"\n", + "# db_type = \"csv\"\n", + "# config = {\n", + "# \"csv_dir\": \"../data/my_research_arcade_data/\"\n", + "# }\n", + "\n", + "# research_arcade = ResearchArcade(db_type=db_type, config=config)" + ] + }, + { + "cell_type": "markdown", + "id": "ea147c5f", + "metadata": {}, + "source": [ + "#### SQL Based" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "fa5a88db", + "metadata": {}, + "outputs": [], + "source": [ + "db_type = \"sql\"\n", "config = {\n", - " \"csv_dir\": \"../data/my_research_arcade_data/\"\n", + " \"host\": \"localhost\",\n", + " \"dbname\": \"postgres\",\n", + " \"user\": \"cl195\",\n", + " \"password\": \"\",\n", + " \"port\": \"5433\"\n", "}\n", "\n", "research_arcade = ResearchArcade(db_type=db_type, config=config)" @@ -107,12 +134,20 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "id": "ccaeefb3", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Paper 2505.23559 does not have metadata downloaded\n" + ] + } + ], "source": [ - "config = {\"arxiv_ids\": [\"1806.08804v4\", \"1903.03894v4\"], \"dest_dir\": \"./download\"}\n", + "config = {\"arxiv_ids\": [\"2505.23559\", \"1903.03894v4\"], \"dest_dir\": \"./download\"}\n", "research_arcade.construct_table_from_api(\"arxiv_papers\", config)" ] }, @@ -125,14 +160,14 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Successfully imported 1 papers from ./examples/csv_data/csv_arxiv_papers_example.csv\n" + "Error: CSV file ./examples/csv_data/csv_arxiv_papers_example.csv does not exist.\n" ] } ], @@ -150,14 +185,14 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "No new papers to import (all papers already exist)\n" + "Error: JSON file ./examples/json_data/json_arxiv_papers_example.json does not exist.\n" ] } ], @@ -176,7 +211,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 9, "id": "insert-paper", "metadata": {}, "outputs": [ @@ -206,7 +241,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 10, "id": "insert-paper-bert", "metadata": {}, "outputs": [ @@ -244,7 +279,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "get-all-papers", "metadata": {}, "outputs": [ @@ -252,43 +287,20 @@ "name": "stdout", "output_type": "stream", "text": [ - "Total papers in database: 7\n", - "\n", - "First 5 papers:\n", - " id arxiv_id base_arxiv_id version \\\n", - "0 2 1810.04805v2 1810.04805 2 \n", - "1 3 1409.0473v7 1409.04730 7 \n", - "2 4 1512.03385v1 1512.03385 1 \n", - "3 5 2010.11929v2 2010.11929 2 \n", - "4 6 1806.08804v4 1806.08804 4 \n", - "\n", - " title \\\n", - "0 BERT: Pre-training of Deep Bidirectional Trans... \n", - "1 Neural Machine Translation by Jointly Learning... \n", - "2 Deep Residual Learning for Image Recognition \n", - "3 An Image is Worth 16x16 Words: Transformers fo... \n", - "4 Hierarchical Graph Representation Learning wit... \n", - "\n", - " abstract \\\n", - "0 We introduce a new language representation mod... \n", - "1 Neural machine translation is a recently propo... \n", - "2 Deeper neural networks are more difficult to t... \n", - "3 We show that a pure transformer applied direct... \n", - "4 Recently, graph neural networks (GNNs) have re... \n", - "\n", - " submit_date \\\n", - "0 2018-10-11 \n", - "1 2014-09-01 \n", - "2 2015-12-10 \n", - "3 2020-10-22 \n", - "4 2018-06-22 18:04:46+00:00 \n", + "Total papers in database: 3\n", "\n", - " metadata \n", - "0 {\"venue\": \"NAACL 2019\", \"citations\": 60000} \n", - "1 {\"venue\": \"ICLR 2015\", \"citations\": 30000} \n", - "2 {\"venue\": \"CVPR 2016\", \"citations\": 70000} \n", - "3 {\"venue\": \"ICLR 2021\", \"citations\": 15000} \n", - "4 {\"id\": \"1806.08804v4\", \"title\": \"Hierarchical ... \n" + "First 5 papers:\n" + ] + }, + { + "ename": "AttributeError", + "evalue": "'list' object has no attribute 'head'", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mAttributeError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[11]\u001b[39m\u001b[32m, line 4\u001b[39m\n\u001b[32m 2\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mTotal papers in database: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(arxiv_papers_df)\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n\u001b[32m 3\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[33mFirst 5 papers:\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m----> \u001b[39m\u001b[32m4\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[43marxiv_papers_df\u001b[49m\u001b[43m.\u001b[49m\u001b[43mhead\u001b[49m())\n", + "\u001b[31mAttributeError\u001b[39m: 'list' object has no attribute 'head'" ] } ], @@ -296,7 +308,7 @@ "arxiv_papers_df = research_arcade.get_all_node_features(\"arxiv_papers\")\n", "print(f\"Total papers in database: {len(arxiv_papers_df)}\")\n", "print(\"\\nFirst 5 papers:\")\n", - "print(arxiv_papers_df.head())" + "print(arxiv_papers_df)" ] }, { @@ -309,7 +321,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "get-paper-by-id", "metadata": {}, "outputs": [ @@ -339,7 +351,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "update-paper", "metadata": {}, "outputs": [ @@ -377,7 +389,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "delete-paper", "metadata": {}, "outputs": [ @@ -422,7 +434,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "c18c7737", "metadata": {}, "outputs": [], @@ -440,7 +452,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -465,7 +477,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -491,7 +503,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "insert-authors", "metadata": {}, "outputs": [ @@ -552,7 +564,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "get-all-authors", "metadata": {}, "outputs": [ @@ -696,7 +708,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "id": "get-author-by-id", "metadata": {}, "outputs": [ @@ -726,7 +738,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "update-author", "metadata": {}, "outputs": [ @@ -771,7 +783,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "id": "168633f2", "metadata": {}, "outputs": [ @@ -798,7 +810,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -823,7 +835,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -849,7 +861,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "id": "insert-categories", "metadata": {}, "outputs": [ @@ -904,7 +916,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "id": "get-all-categories", "metadata": {}, "outputs": [ @@ -963,7 +975,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "id": "insert-figures", "metadata": {}, "outputs": [ @@ -1018,7 +1030,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "id": "get-all-figures", "metadata": {}, "outputs": [ @@ -1090,7 +1102,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "id": "54a13d98", "metadata": {}, "outputs": [ @@ -1173,7 +1185,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -1199,7 +1211,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "id": "b809fdd9", "metadata": {}, "outputs": [ @@ -1254,7 +1266,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "id": "1f1357fc", "metadata": {}, "outputs": [ @@ -1313,7 +1325,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "id": "195e218d", "metadata": {}, "outputs": [ @@ -1396,7 +1408,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -1421,7 +1433,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -1447,7 +1459,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "id": "insert-tables", "metadata": {}, "outputs": [ @@ -1502,7 +1514,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "id": "get-all-tables", "metadata": {}, "outputs": [ @@ -1584,7 +1596,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "id": "c1735fc6", "metadata": {}, "outputs": [ @@ -1667,7 +1679,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -1692,7 +1704,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -1718,7 +1730,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "id": "insert-sections", "metadata": {}, "outputs": [ @@ -1797,7 +1809,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": null, "id": "get-all-sections", "metadata": {}, "outputs": [ @@ -1998,7 +2010,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": null, "id": "480dabdf", "metadata": {}, "outputs": [ @@ -2127,7 +2139,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -2152,7 +2164,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -2178,7 +2190,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": null, "id": "insert-paragraphs", "metadata": {}, "outputs": [ @@ -2254,7 +2266,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": null, "id": "get-all-paragraphs", "metadata": {}, "outputs": [ @@ -2309,7 +2321,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -2341,7 +2353,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -2366,7 +2378,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -2391,7 +2403,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -2437,7 +2449,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -2475,7 +2487,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -2509,7 +2521,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -2546,7 +2558,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -2579,7 +2591,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -2604,7 +2616,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -2629,7 +2641,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -2666,7 +2678,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -2713,7 +2725,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -2742,7 +2754,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -2775,7 +2787,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -2808,7 +2820,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -2833,7 +2845,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -2858,7 +2870,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -2890,7 +2902,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -2924,7 +2936,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -2957,7 +2969,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -2990,7 +3002,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -3021,7 +3033,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -3046,7 +3058,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -3071,7 +3083,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -3109,7 +3121,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -3140,7 +3152,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -3165,7 +3177,7 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -3190,7 +3202,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -3223,7 +3235,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -3244,7 +3256,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -3269,7 +3281,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -3294,7 +3306,7 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -3367,7 +3379,7 @@ ], "metadata": { "kernelspec": { - "display_name": "research_arcade", + "display_name": "vllm312", "language": "python", "name": "python3" }, From 52ac66d1ad1b4e073e8b703e47d5cff1b4a58cfb Mon Sep 17 00:00:00 2001 From: Chongshan0Lin Date: Sun, 9 Nov 2025 11:45:33 -0600 Subject: [PATCH 02/11] implemented sql methods correponding to csv methods --- .../csv_database/csv_arxiv_paper_tables.py | 400 +++++++++--------- .../sql_database/sql_arxiv_authors.py | 96 ++++- .../sql_database/sql_arxiv_categories.py | 101 ++++- .../sql_database/sql_arxiv_figures.py | 111 ++++- .../sql_database/sql_arxiv_paragraphs.py | 2 +- 5 files changed, 501 insertions(+), 209 deletions(-) diff --git a/research_arcade/csv_database/csv_arxiv_paper_tables.py b/research_arcade/csv_database/csv_arxiv_paper_tables.py index 7f5de6b..7eb0f3a 100644 --- a/research_arcade/csv_database/csv_arxiv_paper_tables.py +++ b/research_arcade/csv_database/csv_arxiv_paper_tables.py @@ -1,193 +1,182 @@ -import pandas as pd +import psycopg2 +import psycopg2.extras import os -from pathlib import Path -from typing import Optional +import sys import json - -class CSVArxivPaperTable: - def __init__(self, csv_dir: str): - csv_path = f"{csv_dir}/arxiv_paper_tables.csv" - self.csv_path = csv_path - Path(csv_path).parent.mkdir(parents=True, exist_ok=True) - if not os.path.exists(csv_path): - self.create_paper_tables_table() +sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) +from ..arxiv_utils.multi_input.multi_download import MultiDownload +from ..arxiv_utils.graph_constructor.node_processor import NodeConstructor +from ..arxiv_utils.utils import arxiv_id_processor + + +class SQLArxivPaperTable: + def __init__(self, host: str, dbname: str, user: str, password: str, port: str): + self.host = host + self.dbname = dbname + self.user = user + self.password = password + self.port = port + self.autocommit = True + self.create_paper_tables_table() + + def _get_connection(self): + conn = psycopg2.connect( + host=self.host, + port=self.port, + dbname=self.dbname, + user=self.user, + password=self.password, + ) + conn.autocommit = self.autocommit + return conn def create_paper_tables_table(self): - df = pd.DataFrame(columns=['paper_arxiv_id', 'table_id']) - df.to_csv(self.csv_path, index=False) - print(f"Created paper_tables CSV at {self.csv_path}") - - def _load_data(self): - return pd.read_csv(self.csv_path) if os.path.exists(self.csv_path) else pd.DataFrame() - def _save_data(self, df): - df.to_csv(self.csv_path, index=False) + """ + Create the arxiv_paper_tables table with a composite uniqueness constraint. + """ + conn = self._get_connection() + try: + cur = conn.cursor() + cur.execute(""" + CREATE TABLE IF NOT EXISTS arxiv_paper_tables ( + paper_arxiv_id VARCHAR(100) NOT NULL, + table_id INTEGER NOT NULL + ) + """) + # Composite unique index mirrors CSV conflict check + cur.execute(""" + CREATE UNIQUE INDEX IF NOT EXISTS ux_arxiv_paper_tables_unique + ON arxiv_paper_tables (paper_arxiv_id, table_id) + """) + cur.close() + finally: + conn.close() def insert_paper_table(self, paper_arxiv_id, table_id): - df = self._load_data() - conflict = df[ - (df['paper_arxiv_id'] == paper_arxiv_id) & - (df['table_id'] == table_id) - ] - if not conflict.empty: - return False - new_row = pd.DataFrame([{ - 'paper_arxiv_id': paper_arxiv_id, - 'table_id': table_id - }]) - df = pd.concat([df, new_row], ignore_index=True) - self._save_data(df) - return True + """ + Insert a (paper_arxiv_id, table_id) edge. + Returns True if inserted, False if the pair already exists. + """ + conn = self._get_connection() + try: + cur = conn.cursor() + cur.execute( + """ + INSERT INTO arxiv_paper_tables (paper_arxiv_id, table_id) + VALUES (%s, %s) + ON CONFLICT ON CONSTRAINT ux_arxiv_paper_tables_unique DO NOTHING + """, + (paper_arxiv_id, table_id) + ) + inserted = cur.rowcount > 0 # rowcount==1 if inserted, 0 if conflict + cur.close() + return inserted + finally: + conn.close() def get_all_paper_tables(self): - df = self._load_data() - - if df.empty: - return None - - return df.copy() - - - def get_paper_neighboring_tables(self, paper_arxiv_id: str) -> Optional[pd.DataFrame]: - - df = self._load_data() - - if df.empty: - return None - - # Filter for the specific paper - result = df[df['paper_arxiv_id'] == paper_arxiv_id].copy() - - if result.empty: - return None - - return result.reset_index(drop=True) - - - def get_table_neighboring_papers(self, table_id: int) -> Optional[pd.DataFrame]: - - df = self._load_data() - - if df.empty: - return None - - result = df[df['table_id'] == table_id].copy() - - if result.empty: - return None - - return result.reset_index(drop=True) - + """Get all paper-table relationships from the database.""" + conn = self._get_connection() + try: + cur = conn.cursor() + cur.execute( + "SELECT paper_arxiv_id, table_id FROM arxiv_paper_tables" + ) + rows = cur.fetchall() + cur.close() + return rows if rows else None + finally: + conn.close() + + def get_paper_neighboring_tables(self, paper_arxiv_id: str): + """ + Get all tables for a specific paper. + Returns list of tuples: (paper_arxiv_id, table_id) + """ + conn = self._get_connection() + try: + cur = conn.cursor() + cur.execute( + "SELECT paper_arxiv_id, table_id FROM arxiv_paper_tables WHERE paper_arxiv_id = %s", + (paper_arxiv_id,) + ) + rows = cur.fetchall() + cur.close() + return rows if rows else None + finally: + conn.close() + + def get_table_neighboring_papers(self, table_id: int): + """ + Get all papers that contain a specific table. + Returns list of tuples: (paper_arxiv_id, table_id) + """ + conn = self._get_connection() + try: + cur = conn.cursor() + cur.execute( + "SELECT paper_arxiv_id, table_id FROM arxiv_paper_tables WHERE table_id = %s", + (table_id,) + ) + rows = cur.fetchall() + cur.close() + return rows if rows else None + finally: + conn.close() def delete_paper_table_by_id(self, paper_arxiv_id: str, table_id: int) -> bool: - - df = self._load_data() - - if df.empty: - return False - - mask = (df['paper_arxiv_id'] == paper_arxiv_id) & (df['table_id'] == table_id) - - if not mask.any(): - return False - - df = df[~mask] - self._save_data(df) - - return True - + """ + Delete a specific paper-table relationship. + Returns True if deleted, False if not found. + """ + conn = self._get_connection() + try: + cur = conn.cursor() + cur.execute( + "DELETE FROM arxiv_paper_tables WHERE paper_arxiv_id = %s AND table_id = %s RETURNING paper_arxiv_id", + (paper_arxiv_id, table_id) + ) + deleted = cur.fetchone() is not None + cur.close() + return deleted + finally: + conn.close() def delete_paper_table_by_paper_id(self, paper_arxiv_id: str) -> int: - - df = self._load_data() - - if df.empty: - return 0 - - mask = df['paper_arxiv_id'] == paper_arxiv_id - count = mask.sum() - - if count == 0: - return 0 - - df = df[~mask] - self._save_data(df) - - return count - + """ + Delete all table relationships for a specific paper. + Returns count of deleted rows. + """ + conn = self._get_connection() + try: + cur = conn.cursor() + cur.execute( + "DELETE FROM arxiv_paper_tables WHERE paper_arxiv_id = %s", + (paper_arxiv_id,) + ) + count = cur.rowcount + cur.close() + return count + finally: + conn.close() def delete_paper_table_by_table_id(self, table_id: int) -> int: - - df = self._load_data() - - if df.empty: - return 0 - - mask = df['table_id'] == table_id - count = mask.sum() - - if count == 0: - return 0 - - df = df[~mask] - self._save_data(df) - - return count - - - - def construct_table_from_csv(self, csv_file): """ - Construct the paper-table relationships from an external CSV file. - - Args: - csv_file: Path to the CSV file - - Expected CSV format: - - Required columns: paper_arxiv_id, table_id - - Returns: - bool: True if successful, False otherwise + Delete all paper relationships for a specific table. + Returns count of deleted rows. """ - if not os.path.exists(csv_file): - print(f"Error: CSV file {csv_file} does not exist.") - return False - + conn = self._get_connection() try: - external_df = pd.read_csv(csv_file) - current_df = self._load_data() - - required_cols = ['paper_arxiv_id', 'table_id'] - missing_cols = [col for col in required_cols if col not in external_df.columns] - - if missing_cols: - print(f"Error: External CSV is missing required columns: {missing_cols}") - return False - - # Filter out relationships that already exist - if not current_df.empty: - existing_pairs = set(zip(current_df['paper_arxiv_id'], current_df['table_id'])) - external_df['_pair'] = list(zip(external_df['paper_arxiv_id'], external_df['table_id'])) - external_df = external_df[~external_df['_pair'].isin(existing_pairs)] - external_df = external_df.drop(columns=['_pair']) - - if external_df.empty: - print("No new paper-table relationships to import") - return True - - # Ensure correct column order - external_df = external_df[['paper_arxiv_id', 'table_id']] - - # Combine and save - combined_df = pd.concat([current_df, external_df], ignore_index=True) - self._save_data(combined_df) - - print(f"Successfully imported {len(external_df)} paper-table relationships from {csv_file}") - return True - - except Exception as e: - print(f"Error importing paper-table relationships from CSV: {e}") - return False - + cur = conn.cursor() + cur.execute( + "DELETE FROM arxiv_paper_tables WHERE table_id = %s", + (table_id,) + ) + count = cur.rowcount + cur.close() + return count + finally: + conn.close() def construct_table_from_json(self, json_file): """ @@ -231,37 +220,40 @@ def construct_table_from_json(self, json_file): print("Error: No paper-table data found in JSON file") return False - # Convert to DataFrame - external_df = pd.DataFrame(relations_list) - current_df = self._load_data() - - # Check for required columns - required_cols = ['paper_arxiv_id', 'table_id'] - missing_cols = [col for col in required_cols if col not in external_df.columns] - - if missing_cols: - print(f"Error: JSON data is missing required fields: {missing_cols}") + # Convert to list of tuples for bulk insert + rows = [] + for relation in relations_list: + if 'paper_arxiv_id' not in relation or 'table_id' not in relation: + print(f"Warning: Skipping relation missing required fields: {relation}") + continue + + rows.append(( + relation['paper_arxiv_id'], + relation['table_id'] + )) + + if not rows: + print("No valid paper-table records to import") return False - # Filter out relationships that already exist - if not current_df.empty: - existing_pairs = set(zip(current_df['paper_arxiv_id'], current_df['table_id'])) - external_df['_pair'] = list(zip(external_df['paper_arxiv_id'], external_df['table_id'])) - external_df = external_df[~external_df['_pair'].isin(existing_pairs)] - external_df = external_df.drop(columns=['_pair']) - - if external_df.empty: - print("No new paper-table relationships to import") - return True - - # Ensure correct column order - external_df = external_df[['paper_arxiv_id', 'table_id']] - - # Combine and save - combined_df = pd.concat([current_df, external_df], ignore_index=True) - self._save_data(combined_df) - - print(f"Successfully imported {len(external_df)} paper-table relationships from {json_file}") + conn = self._get_connection() + try: + cur = conn.cursor() + psycopg2.extras.execute_values( + cur, + """ + INSERT INTO arxiv_paper_tables (paper_arxiv_id, table_id) + VALUES %s + ON CONFLICT ON CONSTRAINT ux_arxiv_paper_tables_unique DO NOTHING + """, + rows, + page_size=1000 + ) + cur.close() + finally: + conn.close() + + print(f"Successfully imported {len(rows)} paper-table relationships from {json_file}") return True except json.JSONDecodeError as e: @@ -269,6 +261,4 @@ def construct_table_from_json(self, json_file): return False except Exception as e: print(f"Error importing paper-table relationships from JSON: {e}") - return False - - + return False \ No newline at end of file diff --git a/research_arcade/sql_database/sql_arxiv_authors.py b/research_arcade/sql_database/sql_arxiv_authors.py index 0ceb8e7..1cf0af0 100644 --- a/research_arcade/sql_database/sql_arxiv_authors.py +++ b/research_arcade/sql_database/sql_arxiv_authors.py @@ -341,4 +341,98 @@ def construct_table_from_json(self, json_file): return False except Exception as e: print(f"Error importing authors from JSON: {e}") - return False \ No newline at end of file + return False + + + def get_author_by_semantic_scholar_id(self, semantic_scholar_id: str, return_all: bool = False): + """ + Get an author by its semantic_scholar_id. + - If return_all=False, returns a single row tuple (id, semantic_scholar_id, name, homepage). + - If return_all=True, returns all matching rows as a list of tuples. + Returns None if not found. + """ + conn = self._get_connection() + try: + cur = conn.cursor() + cur.execute( + "SELECT id, semantic_scholar_id, name, homepage FROM authors WHERE semantic_scholar_id = %s", + (semantic_scholar_id,), + ) + res = cur.fetchall() if return_all else cur.fetchone() + cur.close() + return res if res else None + finally: + conn.close() + + + def check_author_exists_by_semantic_scholar_id(self, semantic_scholar_id: str) -> bool: + """Check if an author exists by its semantic_scholar_id.""" + conn = self._get_connection() + try: + cur = conn.cursor() + cur.execute("SELECT 1 FROM authors WHERE semantic_scholar_id = %s LIMIT 1", (semantic_scholar_id,)) + exists = cur.fetchone() is not None + cur.close() + return exists + finally: + conn.close() + + + def get_author_id_by_semantic_scholar_id(self, semantic_scholar_id: str) -> int: + """ + Get the internal database id for an author by semantic_scholar_id. + Returns None if not found. + """ + conn = self._get_connection() + try: + cur = conn.cursor() + cur.execute("SELECT id FROM authors WHERE semantic_scholar_id = %s", (semantic_scholar_id,)) + result = cur.fetchone() + cur.close() + return result[0] if result else None + finally: + conn.close() + + + def delete_author_by_semantic_scholar_id(self, semantic_scholar_id: str) -> bool: + """Delete an author by its semantic_scholar_id. Returns True if deleted, False if not found.""" + conn = self._get_connection() + try: + cur = conn.cursor() + cur.execute("DELETE FROM authors WHERE semantic_scholar_id = %s RETURNING id", (semantic_scholar_id,)) + deleted = cur.fetchone() is not None + cur.close() + return deleted + finally: + conn.close() + + + def update_author_by_semantic_scholar_id(self, semantic_scholar_id: str, name=None, homepage=None) -> bool: + """ + Update an author by semantic_scholar_id. Returns True if updated, False if not found or no fields provided. + """ + fields = [] + values = [] + + if name is not None: + fields.append("name = %s") + values.append(name) + if homepage is not None: + fields.append("homepage = %s") + values.append(homepage) + + if not fields: + return False # Nothing to update + + sql = f"UPDATE authors SET {', '.join(fields)} WHERE semantic_scholar_id = %s RETURNING id" + values.append(semantic_scholar_id) + + conn = self._get_connection() + try: + cur = conn.cursor() + cur.execute(sql, tuple(values)) + updated = cur.fetchone() is not None + cur.close() + return updated + finally: + conn.close() diff --git a/research_arcade/sql_database/sql_arxiv_categories.py b/research_arcade/sql_database/sql_arxiv_categories.py index c3c88b0..b9c3c93 100644 --- a/research_arcade/sql_database/sql_arxiv_categories.py +++ b/research_arcade/sql_database/sql_arxiv_categories.py @@ -348,4 +348,103 @@ def construct_table_from_json(self, json_file): return False except Exception as e: print(f"Error importing categories from JSON: {e}") - return False \ No newline at end of file + return False + + + def get_category_by_name(self, name: str, return_all: bool = False): + """ + Get a category by its name. + - If return_all=False, returns a single tuple (id, name, description) + - If return_all=True, returns a list of such tuples. + Returns None if not found. + """ + conn = self._get_connection() + try: + cur = conn.cursor() + cur.execute( + "SELECT id, name, description FROM arxiv_categories WHERE name = %s", + (name,) + ) + rows = cur.fetchall() if return_all else cur.fetchone() + cur.close() + return rows if rows else None + finally: + conn.close() + + + def check_category_exists_by_name(self, name: str) -> bool: + """ + True if a category with the given name exists. + """ + conn = self._get_connection() + try: + cur = conn.cursor() + cur.execute("SELECT 1 FROM arxiv_categories WHERE name = %s LIMIT 1", (name,)) + ok = cur.fetchone() is not None + cur.close() + return ok + finally: + conn.close() + + + def get_category_id_by_name(self, name: str) -> int: + """ + Get the internal database id for a category by name. + Returns None if not found. + """ + conn = self._get_connection() + try: + cur = conn.cursor() + cur.execute("SELECT id FROM arxiv_categories WHERE name = %s", (name,)) + result = cur.fetchone() + cur.close() + return result[0] if result else None + finally: + conn.close() + + + def delete_category_by_name(self, name: str) -> bool: + """ + Delete by name; returns True if a row was deleted. + """ + conn = self._get_connection() + try: + cur = conn.cursor() + cur.execute("DELETE FROM arxiv_categories WHERE name = %s RETURNING id", (name,)) + ok = cur.fetchone() is not None + cur.close() + return ok + finally: + conn.close() + + + def update_category_by_name(self, name: str, new_name: str = None, description: str = None) -> bool: + """ + Partial update by name. Only non-None fields are updated. + Returns True if a row was updated. + """ + sets = [] + vals = [] + + if new_name is not None: + sets.append("name = %s") + vals.append(new_name) + if description is not None: + sets.append("description = %s") + vals.append(description) + + if not sets: + return False + + sql = f"UPDATE arxiv_categories SET {', '.join(sets)} WHERE name = %s RETURNING id" + vals.append(name) + + conn = self._get_connection() + try: + cur = conn.cursor() + cur.execute(sql, tuple(vals)) + ok = cur.fetchone() is not None + cur.close() + return ok + finally: + conn.close() \ No newline at end of file diff --git a/research_arcade/sql_database/sql_arxiv_figures.py b/research_arcade/sql_database/sql_arxiv_figures.py index e3d971c..65edae6 100644 --- a/research_arcade/sql_database/sql_arxiv_figures.py +++ b/research_arcade/sql_database/sql_arxiv_figures.py @@ -401,4 +401,113 @@ def construct_figures_table_from_api(self, arxiv_ids, dest_dir): continue except Exception as e: print(f"An unexpected error occurred: {e}") - continue \ No newline at end of file + continue + + + + def get_figure_by_name(self, name: str, return_all: bool = False): + """ + Get a figure by its name (when name is not NULL). + - If return_all=False, returns a single tuple + (id, paper_arxiv_id, path, caption, label, name) + - If return_all=True, returns a list of such tuples. + Returns None if not found. + """ + conn = self._get_connection() + try: + cur = conn.cursor() + cur.execute( + "SELECT id, paper_arxiv_id, path, caption, label, name " + "FROM arxiv_figures WHERE name = %s", + (name,) + ) + rows = cur.fetchall() if return_all else cur.fetchone() + cur.close() + return rows if rows else None + finally: + conn.close() + + + def check_figure_exists_by_name(self, name: str) -> bool: + """ + Returns True if a figure with the given name exists. + """ + conn = self._get_connection() + try: + cur = conn.cursor() + cur.execute("SELECT 1 FROM arxiv_figures WHERE name = %s LIMIT 1", (name,)) + ok = cur.fetchone() is not None + cur.close() + return ok + finally: + conn.close() + + + def get_figure_id_by_name(self, name: str) -> int: + """ + Get the internal database id for a figure by name. + Returns None if not found. + """ + conn = self._get_connection() + try: + cur = conn.cursor() + cur.execute("SELECT id FROM arxiv_figures WHERE name = %s", (name,)) + result = cur.fetchone() + cur.close() + return result[0] if result else None + finally: + conn.close() + + + def delete_figure_by_name(self, name: str) -> bool: + """ + Delete by name; returns True if a row was deleted. + """ + conn = self._get_connection() + try: + cur = conn.cursor() + cur.execute("DELETE FROM arxiv_figures WHERE name = %s RETURNING id", (name,)) + ok = cur.fetchone() is not None + cur.close() + return ok + finally: + conn.close() + + + def get_figures_by_paper(self, paper_arxiv_id: str): + """ + Get all figures for a specific paper. + Returns a list of tuples (id, paper_arxiv_id, path, caption, label, name). + """ + conn = self._get_connection() + try: + cur = conn.cursor() + cur.execute( + "SELECT id, paper_arxiv_id, path, caption, label, name " + "FROM arxiv_figures WHERE paper_arxiv_id = %s", + (paper_arxiv_id,) + ) + rows = cur.fetchall() + cur.close() + return rows if rows else None + finally: + conn.close() + + + def delete_figures_by_paper(self, paper_arxiv_id: str) -> int: + """ + Delete all figures for a specific paper. + Returns the count of deleted rows. + """ + conn = self._get_connection() + try: + cur = conn.cursor() + cur.execute( + "DELETE FROM arxiv_figures WHERE paper_arxiv_id = %s", + (paper_arxiv_id,) + ) + count = cur.rowcount + cur.close() + return count + finally: + conn.close() \ No newline at end of file diff --git a/research_arcade/sql_database/sql_arxiv_paragraphs.py b/research_arcade/sql_database/sql_arxiv_paragraphs.py index 3477453..34e1fa0 100644 --- a/research_arcade/sql_database/sql_arxiv_paragraphs.py +++ b/research_arcade/sql_database/sql_arxiv_paragraphs.py @@ -282,7 +282,7 @@ def construct_paragraph_table_from_csv(self, csv_file: str) -> bool: if not os.path.exists(csv_file): print(f"Error: CSV file {csv_file} does not exist.") return False - + df = pd.read_csv(csv_file) required_cols = ['paragraph_id', 'content', 'paper_arxiv_id', 'paper_section'] missing = [c for c in required_cols if c not in df.columns] From 99c767962dd1318e81c4b10eb0dd32513e6ffe3a Mon Sep 17 00:00:00 2001 From: Chongshan0Lin Date: Sun, 9 Nov 2025 12:25:36 -0600 Subject: [PATCH 03/11] Adjusting sql database methods --- .../csv_database/csv_arxiv_paper_tables.py | 400 +++--- research_arcade/research_arcade.py | 6 +- .../sql_database/sql_arxiv_authors.py | 10 +- .../sql_database/sql_arxiv_figures.py | 2 +- .../sql_database/sql_arxiv_paper_authors.py | 8 +- .../sql_database/sql_arxiv_paper_tables.py | 194 ++- .../sql_database/sql_arxiv_paragraphs.py | 9 +- .../sql_database/sql_arxiv_sections.py | 2 +- ...rcade_complete_tutorial_with_imports.ipynb | 1088 +++++------------ 9 files changed, 706 insertions(+), 1013 deletions(-) diff --git a/research_arcade/csv_database/csv_arxiv_paper_tables.py b/research_arcade/csv_database/csv_arxiv_paper_tables.py index 7eb0f3a..7f5de6b 100644 --- a/research_arcade/csv_database/csv_arxiv_paper_tables.py +++ b/research_arcade/csv_database/csv_arxiv_paper_tables.py @@ -1,182 +1,193 @@ -import psycopg2 -import psycopg2.extras +import pandas as pd import os -import sys +from pathlib import Path +from typing import Optional import json -sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) -from ..arxiv_utils.multi_input.multi_download import MultiDownload -from ..arxiv_utils.graph_constructor.node_processor import NodeConstructor -from ..arxiv_utils.utils import arxiv_id_processor - - -class SQLArxivPaperTable: - def __init__(self, host: str, dbname: str, user: str, password: str, port: str): - self.host = host - self.dbname = dbname - self.user = user - self.password = password - self.port = port - self.autocommit = True - self.create_paper_tables_table() - - def _get_connection(self): - conn = psycopg2.connect( - host=self.host, - port=self.port, - dbname=self.dbname, - user=self.user, - password=self.password, - ) - conn.autocommit = self.autocommit - return conn + +class CSVArxivPaperTable: + def __init__(self, csv_dir: str): + csv_path = f"{csv_dir}/arxiv_paper_tables.csv" + self.csv_path = csv_path + Path(csv_path).parent.mkdir(parents=True, exist_ok=True) + if not os.path.exists(csv_path): + self.create_paper_tables_table() def create_paper_tables_table(self): - """ - Create the arxiv_paper_tables table with a composite uniqueness constraint. - """ - conn = self._get_connection() - try: - cur = conn.cursor() - cur.execute(""" - CREATE TABLE IF NOT EXISTS arxiv_paper_tables ( - paper_arxiv_id VARCHAR(100) NOT NULL, - table_id INTEGER NOT NULL - ) - """) - # Composite unique index mirrors CSV conflict check - cur.execute(""" - CREATE UNIQUE INDEX IF NOT EXISTS ux_arxiv_paper_tables_unique - ON arxiv_paper_tables (paper_arxiv_id, table_id) - """) - cur.close() - finally: - conn.close() + df = pd.DataFrame(columns=['paper_arxiv_id', 'table_id']) + df.to_csv(self.csv_path, index=False) + print(f"Created paper_tables CSV at {self.csv_path}") + + def _load_data(self): + return pd.read_csv(self.csv_path) if os.path.exists(self.csv_path) else pd.DataFrame() + def _save_data(self, df): + df.to_csv(self.csv_path, index=False) def insert_paper_table(self, paper_arxiv_id, table_id): - """ - Insert a (paper_arxiv_id, table_id) edge. - Returns True if inserted, False if the pair already exists. - """ - conn = self._get_connection() - try: - cur = conn.cursor() - cur.execute( - """ - INSERT INTO arxiv_paper_tables (paper_arxiv_id, table_id) - VALUES (%s, %s) - ON CONFLICT ON CONSTRAINT ux_arxiv_paper_tables_unique DO NOTHING - """, - (paper_arxiv_id, table_id) - ) - inserted = cur.rowcount > 0 # rowcount==1 if inserted, 0 if conflict - cur.close() - return inserted - finally: - conn.close() + df = self._load_data() + conflict = df[ + (df['paper_arxiv_id'] == paper_arxiv_id) & + (df['table_id'] == table_id) + ] + if not conflict.empty: + return False + new_row = pd.DataFrame([{ + 'paper_arxiv_id': paper_arxiv_id, + 'table_id': table_id + }]) + df = pd.concat([df, new_row], ignore_index=True) + self._save_data(df) + return True def get_all_paper_tables(self): - """Get all paper-table relationships from the database.""" - conn = self._get_connection() - try: - cur = conn.cursor() - cur.execute( - "SELECT paper_arxiv_id, table_id FROM arxiv_paper_tables" - ) - rows = cur.fetchall() - cur.close() - return rows if rows else None - finally: - conn.close() - - def get_paper_neighboring_tables(self, paper_arxiv_id: str): - """ - Get all tables for a specific paper. - Returns list of tuples: (paper_arxiv_id, table_id) - """ - conn = self._get_connection() - try: - cur = conn.cursor() - cur.execute( - "SELECT paper_arxiv_id, table_id FROM arxiv_paper_tables WHERE paper_arxiv_id = %s", - (paper_arxiv_id,) - ) - rows = cur.fetchall() - cur.close() - return rows if rows else None - finally: - conn.close() - - def get_table_neighboring_papers(self, table_id: int): - """ - Get all papers that contain a specific table. - Returns list of tuples: (paper_arxiv_id, table_id) - """ - conn = self._get_connection() - try: - cur = conn.cursor() - cur.execute( - "SELECT paper_arxiv_id, table_id FROM arxiv_paper_tables WHERE table_id = %s", - (table_id,) - ) - rows = cur.fetchall() - cur.close() - return rows if rows else None - finally: - conn.close() + df = self._load_data() + + if df.empty: + return None + + return df.copy() + + + def get_paper_neighboring_tables(self, paper_arxiv_id: str) -> Optional[pd.DataFrame]: + + df = self._load_data() + + if df.empty: + return None + + # Filter for the specific paper + result = df[df['paper_arxiv_id'] == paper_arxiv_id].copy() + + if result.empty: + return None + + return result.reset_index(drop=True) + + + def get_table_neighboring_papers(self, table_id: int) -> Optional[pd.DataFrame]: + + df = self._load_data() + + if df.empty: + return None + + result = df[df['table_id'] == table_id].copy() + + if result.empty: + return None + + return result.reset_index(drop=True) + def delete_paper_table_by_id(self, paper_arxiv_id: str, table_id: int) -> bool: - """ - Delete a specific paper-table relationship. - Returns True if deleted, False if not found. - """ - conn = self._get_connection() - try: - cur = conn.cursor() - cur.execute( - "DELETE FROM arxiv_paper_tables WHERE paper_arxiv_id = %s AND table_id = %s RETURNING paper_arxiv_id", - (paper_arxiv_id, table_id) - ) - deleted = cur.fetchone() is not None - cur.close() - return deleted - finally: - conn.close() + + df = self._load_data() + + if df.empty: + return False + + mask = (df['paper_arxiv_id'] == paper_arxiv_id) & (df['table_id'] == table_id) + + if not mask.any(): + return False + + df = df[~mask] + self._save_data(df) + + return True + def delete_paper_table_by_paper_id(self, paper_arxiv_id: str) -> int: - """ - Delete all table relationships for a specific paper. - Returns count of deleted rows. - """ - conn = self._get_connection() - try: - cur = conn.cursor() - cur.execute( - "DELETE FROM arxiv_paper_tables WHERE paper_arxiv_id = %s", - (paper_arxiv_id,) - ) - count = cur.rowcount - cur.close() - return count - finally: - conn.close() + + df = self._load_data() + + if df.empty: + return 0 + + mask = df['paper_arxiv_id'] == paper_arxiv_id + count = mask.sum() + + if count == 0: + return 0 + + df = df[~mask] + self._save_data(df) + + return count + def delete_paper_table_by_table_id(self, table_id: int) -> int: + + df = self._load_data() + + if df.empty: + return 0 + + mask = df['table_id'] == table_id + count = mask.sum() + + if count == 0: + return 0 + + df = df[~mask] + self._save_data(df) + + return count + + + + def construct_table_from_csv(self, csv_file): """ - Delete all paper relationships for a specific table. - Returns count of deleted rows. + Construct the paper-table relationships from an external CSV file. + + Args: + csv_file: Path to the CSV file + + Expected CSV format: + - Required columns: paper_arxiv_id, table_id + + Returns: + bool: True if successful, False otherwise """ - conn = self._get_connection() + if not os.path.exists(csv_file): + print(f"Error: CSV file {csv_file} does not exist.") + return False + try: - cur = conn.cursor() - cur.execute( - "DELETE FROM arxiv_paper_tables WHERE table_id = %s", - (table_id,) - ) - count = cur.rowcount - cur.close() - return count - finally: - conn.close() + external_df = pd.read_csv(csv_file) + current_df = self._load_data() + + required_cols = ['paper_arxiv_id', 'table_id'] + missing_cols = [col for col in required_cols if col not in external_df.columns] + + if missing_cols: + print(f"Error: External CSV is missing required columns: {missing_cols}") + return False + + # Filter out relationships that already exist + if not current_df.empty: + existing_pairs = set(zip(current_df['paper_arxiv_id'], current_df['table_id'])) + external_df['_pair'] = list(zip(external_df['paper_arxiv_id'], external_df['table_id'])) + external_df = external_df[~external_df['_pair'].isin(existing_pairs)] + external_df = external_df.drop(columns=['_pair']) + + if external_df.empty: + print("No new paper-table relationships to import") + return True + + # Ensure correct column order + external_df = external_df[['paper_arxiv_id', 'table_id']] + + # Combine and save + combined_df = pd.concat([current_df, external_df], ignore_index=True) + self._save_data(combined_df) + + print(f"Successfully imported {len(external_df)} paper-table relationships from {csv_file}") + return True + + except Exception as e: + print(f"Error importing paper-table relationships from CSV: {e}") + return False + def construct_table_from_json(self, json_file): """ @@ -220,40 +231,37 @@ def construct_table_from_json(self, json_file): print("Error: No paper-table data found in JSON file") return False - # Convert to list of tuples for bulk insert - rows = [] - for relation in relations_list: - if 'paper_arxiv_id' not in relation or 'table_id' not in relation: - print(f"Warning: Skipping relation missing required fields: {relation}") - continue - - rows.append(( - relation['paper_arxiv_id'], - relation['table_id'] - )) - - if not rows: - print("No valid paper-table records to import") + # Convert to DataFrame + external_df = pd.DataFrame(relations_list) + current_df = self._load_data() + + # Check for required columns + required_cols = ['paper_arxiv_id', 'table_id'] + missing_cols = [col for col in required_cols if col not in external_df.columns] + + if missing_cols: + print(f"Error: JSON data is missing required fields: {missing_cols}") return False - conn = self._get_connection() - try: - cur = conn.cursor() - psycopg2.extras.execute_values( - cur, - """ - INSERT INTO arxiv_paper_tables (paper_arxiv_id, table_id) - VALUES %s - ON CONFLICT ON CONSTRAINT ux_arxiv_paper_tables_unique DO NOTHING - """, - rows, - page_size=1000 - ) - cur.close() - finally: - conn.close() - - print(f"Successfully imported {len(rows)} paper-table relationships from {json_file}") + # Filter out relationships that already exist + if not current_df.empty: + existing_pairs = set(zip(current_df['paper_arxiv_id'], current_df['table_id'])) + external_df['_pair'] = list(zip(external_df['paper_arxiv_id'], external_df['table_id'])) + external_df = external_df[~external_df['_pair'].isin(existing_pairs)] + external_df = external_df.drop(columns=['_pair']) + + if external_df.empty: + print("No new paper-table relationships to import") + return True + + # Ensure correct column order + external_df = external_df[['paper_arxiv_id', 'table_id']] + + # Combine and save + combined_df = pd.concat([current_df, external_df], ignore_index=True) + self._save_data(combined_df) + + print(f"Successfully imported {len(external_df)} paper-table relationships from {json_file}") return True except json.JSONDecodeError as e: @@ -261,4 +269,6 @@ def construct_table_from_json(self, json_file): return False except Exception as e: print(f"Error importing paper-table relationships from JSON: {e}") - return False \ No newline at end of file + return False + + diff --git a/research_arcade/research_arcade.py b/research_arcade/research_arcade.py index 482fc03..9921a18 100644 --- a/research_arcade/research_arcade.py +++ b/research_arcade/research_arcade.py @@ -160,7 +160,7 @@ def delete_node_by_id(self, table: str, primary_key: dict) -> Optional[pd.DataFr elif table == 'arxiv_tables': return self.arxiv_tables.delete_table_by_id(**primary_key) elif table == 'arxiv_papers': - return self.arxiv_papers.delete_paper_by_id(**primary_key) + return self.arxiv_papers.delete_paper_by_arxiv_id(**primary_key) elif table == 'arxiv_paragraphs': return self.arxiv_paragraphs.delete_paragraph_by_id(**primary_key) elif table == 'arxiv_sections': @@ -182,7 +182,7 @@ def update_node(self, table: str, node_features: dict) -> Optional[pd.DataFrame] return self.openreview_revisions.update_revision(**node_features) # Tables in arxiv dataset elif table == 'arxiv_authors': - return self.arxiv_authors.update_author(**node_features) + return self.arxiv_authors.update_author_by_semantic_scholar_id(**node_features) elif table == 'arxiv_categories': return self.arxiv_categories.update_category(**node_features) elif table == 'arxiv_figures': @@ -221,7 +221,7 @@ def get_node_features_by_id(self, table: str, primary_key: dict) -> Optional[pd. elif table == 'arxiv_tables': return self.arxiv_tables.get_table_by_id(**primary_key) elif table == 'arxiv_papers': - return self.arxiv_papers.get_paper_by_id(**primary_key) + return self.arxiv_papers.get_paper_by_arxiv_id(**primary_key) elif table == 'arxiv_paragraphs': return self.arxiv_paragraphs.get_paragraph_by_id(**primary_key) elif table == 'arxiv_sections': diff --git a/research_arcade/sql_database/sql_arxiv_authors.py b/research_arcade/sql_database/sql_arxiv_authors.py index 1cf0af0..9d22c79 100644 --- a/research_arcade/sql_database/sql_arxiv_authors.py +++ b/research_arcade/sql_database/sql_arxiv_authors.py @@ -111,7 +111,7 @@ def update_author(self, id: int, semantic_scholar_id=None, name=None, homepage=N finally: conn.close() - def get_author_by_id(self, id: int, return_all=False): + def get_author_by_id(self, semantic_scholar: int, return_all=False): """ Get an author by its id. - If return_all=False, returns a single row tuple (id, semantic_scholar_id, name, homepage). @@ -121,8 +121,8 @@ def get_author_by_id(self, id: int, return_all=False): try: cur = conn.cursor() cur.execute( - "SELECT id, semantic_scholar_id, name, homepage FROM authors WHERE id = %s", - (id,), + "SELECT id, semantic_scholar_id, name, homepage FROM authors WHERE semantic_scholar = %s", + (semantic_scholar,), ) res = cur.fetchall() if return_all else cur.fetchone() cur.close() @@ -414,7 +414,7 @@ def update_author_by_semantic_scholar_id(self, semantic_scholar_id: str, name=No fields = [] values = [] - if name is not None: + if name is not None: fields.append("name = %s") values.append(name) if homepage is not None: @@ -426,7 +426,7 @@ def update_author_by_semantic_scholar_id(self, semantic_scholar_id: str, name=No sql = f"UPDATE authors SET {', '.join(fields)} WHERE semantic_scholar_id = %s RETURNING id" values.append(semantic_scholar_id) - + conn = self._get_connection() try: cur = conn.cursor() diff --git a/research_arcade/sql_database/sql_arxiv_figures.py b/research_arcade/sql_database/sql_arxiv_figures.py index 65edae6..d15f005 100644 --- a/research_arcade/sql_database/sql_arxiv_figures.py +++ b/research_arcade/sql_database/sql_arxiv_figures.py @@ -81,7 +81,7 @@ def insert_figure(self, paper_arxiv_id, path, caption=None, label=None, name=Non """ INSERT INTO arxiv_figures (paper_arxiv_id, path, caption, label, name) VALUES (%s, %s, %s, %s, %s) - ON CONFLICT ON CONSTRAINT ux_arxiv_figures_name_notnull DO NOTHING + ON CONFLICT (name) WHERE name IS NOT NULL DO NOTHING RETURNING id """, (paper_arxiv_id, path, caption, label, name) diff --git a/research_arcade/sql_database/sql_arxiv_paper_authors.py b/research_arcade/sql_database/sql_arxiv_paper_authors.py index 62f5188..3bfe557 100644 --- a/research_arcade/sql_database/sql_arxiv_paper_authors.py +++ b/research_arcade/sql_database/sql_arxiv_paper_authors.py @@ -46,14 +46,10 @@ def create_paper_authors_table(self): CREATE TABLE IF NOT EXISTS arxiv_paper_authors ( paper_arxiv_id VARCHAR(100) NOT NULL, author_id VARCHAR(100) NOT NULL, - author_sequence INTEGER NOT NULL + author_sequence INTEGER NOT NULL, + CONSTRAINT ux_arxiv_paper_authors_unique UNIQUE (paper_arxiv_id, author_id) ) """) - # Create composite unique index to prevent duplicates - cur.execute(""" - CREATE UNIQUE INDEX IF NOT EXISTS ux_arxiv_paper_authors_unique - ON arxiv_paper_authors (paper_arxiv_id, author_id) - """) cur.close() finally: conn.close() diff --git a/research_arcade/sql_database/sql_arxiv_paper_tables.py b/research_arcade/sql_database/sql_arxiv_paper_tables.py index 7163b4e..7eb0f3a 100644 --- a/research_arcade/sql_database/sql_arxiv_paper_tables.py +++ b/research_arcade/sql_database/sql_arxiv_paper_tables.py @@ -1,11 +1,14 @@ import psycopg2 - +import psycopg2.extras import os import sys +import json sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) from ..arxiv_utils.multi_input.multi_download import MultiDownload from ..arxiv_utils.graph_constructor.node_processor import NodeConstructor from ..arxiv_utils.utils import arxiv_id_processor + + class SQLArxivPaperTable: def __init__(self, host: str, dbname: str, user: str, password: str, port: str): self.host = host @@ -70,3 +73,192 @@ def insert_paper_table(self, paper_arxiv_id, table_id): return inserted finally: conn.close() + + def get_all_paper_tables(self): + """Get all paper-table relationships from the database.""" + conn = self._get_connection() + try: + cur = conn.cursor() + cur.execute( + "SELECT paper_arxiv_id, table_id FROM arxiv_paper_tables" + ) + rows = cur.fetchall() + cur.close() + return rows if rows else None + finally: + conn.close() + + def get_paper_neighboring_tables(self, paper_arxiv_id: str): + """ + Get all tables for a specific paper. + Returns list of tuples: (paper_arxiv_id, table_id) + """ + conn = self._get_connection() + try: + cur = conn.cursor() + cur.execute( + "SELECT paper_arxiv_id, table_id FROM arxiv_paper_tables WHERE paper_arxiv_id = %s", + (paper_arxiv_id,) + ) + rows = cur.fetchall() + cur.close() + return rows if rows else None + finally: + conn.close() + + def get_table_neighboring_papers(self, table_id: int): + """ + Get all papers that contain a specific table. + Returns list of tuples: (paper_arxiv_id, table_id) + """ + conn = self._get_connection() + try: + cur = conn.cursor() + cur.execute( + "SELECT paper_arxiv_id, table_id FROM arxiv_paper_tables WHERE table_id = %s", + (table_id,) + ) + rows = cur.fetchall() + cur.close() + return rows if rows else None + finally: + conn.close() + + def delete_paper_table_by_id(self, paper_arxiv_id: str, table_id: int) -> bool: + """ + Delete a specific paper-table relationship. + Returns True if deleted, False if not found. + """ + conn = self._get_connection() + try: + cur = conn.cursor() + cur.execute( + "DELETE FROM arxiv_paper_tables WHERE paper_arxiv_id = %s AND table_id = %s RETURNING paper_arxiv_id", + (paper_arxiv_id, table_id) + ) + deleted = cur.fetchone() is not None + cur.close() + return deleted + finally: + conn.close() + + def delete_paper_table_by_paper_id(self, paper_arxiv_id: str) -> int: + """ + Delete all table relationships for a specific paper. + Returns count of deleted rows. + """ + conn = self._get_connection() + try: + cur = conn.cursor() + cur.execute( + "DELETE FROM arxiv_paper_tables WHERE paper_arxiv_id = %s", + (paper_arxiv_id,) + ) + count = cur.rowcount + cur.close() + return count + finally: + conn.close() + + def delete_paper_table_by_table_id(self, table_id: int) -> int: + """ + Delete all paper relationships for a specific table. + Returns count of deleted rows. + """ + conn = self._get_connection() + try: + cur = conn.cursor() + cur.execute( + "DELETE FROM arxiv_paper_tables WHERE table_id = %s", + (table_id,) + ) + count = cur.rowcount + cur.close() + return count + finally: + conn.close() + + def construct_table_from_json(self, json_file): + """ + Construct the paper-table relationships from an external JSON file. + + Args: + json_file: Path to the JSON file + + Expected JSON format: + [ + {"paper_arxiv_id": "1706.03762v7", "table_id": 1}, + {"paper_arxiv_id": "1706.03762v7", "table_id": 2}, + ... + ] + + Returns: + bool: True if successful, False otherwise + """ + if not os.path.exists(json_file): + print(f"Error: JSON file {json_file} does not exist.") + return False + + try: + # Load JSON data + with open(json_file, 'r', encoding='utf-8') as f: + json_data = json.load(f) + + # Handle different JSON structures + if isinstance(json_data, dict): + if 'paper_tables' in json_data: + relations_list = json_data['paper_tables'] + else: + relations_list = [json_data] + elif isinstance(json_data, list): + relations_list = json_data + else: + print("Error: JSON file must contain either a list or a dictionary") + return False + + if not relations_list: + print("Error: No paper-table data found in JSON file") + return False + + # Convert to list of tuples for bulk insert + rows = [] + for relation in relations_list: + if 'paper_arxiv_id' not in relation or 'table_id' not in relation: + print(f"Warning: Skipping relation missing required fields: {relation}") + continue + + rows.append(( + relation['paper_arxiv_id'], + relation['table_id'] + )) + + if not rows: + print("No valid paper-table records to import") + return False + + conn = self._get_connection() + try: + cur = conn.cursor() + psycopg2.extras.execute_values( + cur, + """ + INSERT INTO arxiv_paper_tables (paper_arxiv_id, table_id) + VALUES %s + ON CONFLICT ON CONSTRAINT ux_arxiv_paper_tables_unique DO NOTHING + """, + rows, + page_size=1000 + ) + cur.close() + finally: + conn.close() + + print(f"Successfully imported {len(rows)} paper-table relationships from {json_file}") + return True + + except json.JSONDecodeError as e: + print(f"Error: Invalid JSON file - {e}") + return False + except Exception as e: + print(f"Error importing paper-table relationships from JSON: {e}") + return False \ No newline at end of file diff --git a/research_arcade/sql_database/sql_arxiv_paragraphs.py b/research_arcade/sql_database/sql_arxiv_paragraphs.py index 34e1fa0..c309759 100644 --- a/research_arcade/sql_database/sql_arxiv_paragraphs.py +++ b/research_arcade/sql_database/sql_arxiv_paragraphs.py @@ -52,18 +52,13 @@ def create_paragraphs_table(self): paragraph_id VARCHAR(255) NOT NULL, content TEXT, paper_arxiv_id VARCHAR(100) NOT NULL, - paper_section VARCHAR(255) NOT NULL + paper_section VARCHAR(255) NOT NULL, + CONSTRAINT ux_arxiv_paragraphs_unique UNIQUE (paragraph_id, paper_arxiv_id, paper_section) ) """) - # Composite unique index for conflict prevention - cur.execute(""" - CREATE UNIQUE INDEX IF NOT EXISTS ux_arxiv_paragraphs_unique - ON arxiv_paragraphs (paragraph_id, paper_arxiv_id, paper_section) - """) cur.close() finally: conn.close() - # ------------------------- # CRUD # ------------------------- diff --git a/research_arcade/sql_database/sql_arxiv_sections.py b/research_arcade/sql_database/sql_arxiv_sections.py index 582350f..5c9192d 100644 --- a/research_arcade/sql_database/sql_arxiv_sections.py +++ b/research_arcade/sql_database/sql_arxiv_sections.py @@ -55,7 +55,7 @@ def create_sections_table(self): # ------------------------- # CRUD # ------------------------- - def insert_section(self, content, title, is_appendix, paper_arxiv_id, section_in_paper_id=None) -> Optional[int]: + def insert_section(self, content, title, appendix, paper_arxiv_id, section_in_paper_id=None) -> Optional[int]: """ Insert a section; returns generated id or None. """ diff --git a/research_arcade_complete_tutorial_with_imports.ipynb b/research_arcade_complete_tutorial_with_imports.ipynb index f7452f8..8749487 100644 --- a/research_arcade_complete_tutorial_with_imports.ipynb +++ b/research_arcade_complete_tutorial_with_imports.ipynb @@ -66,7 +66,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "169f7a6d", "metadata": {}, "outputs": [], @@ -134,7 +134,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "id": "ccaeefb3", "metadata": {}, "outputs": [ @@ -160,7 +160,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -185,7 +185,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -211,7 +211,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 6, "id": "insert-paper", "metadata": {}, "outputs": [ @@ -241,7 +241,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 7, "id": "insert-paper-bert", "metadata": {}, "outputs": [ @@ -279,7 +279,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "get-all-papers", "metadata": {}, "outputs": [ @@ -289,18 +289,8 @@ "text": [ "Total papers in database: 3\n", "\n", - "First 5 papers:\n" - ] - }, - { - "ename": "AttributeError", - "evalue": "'list' object has no attribute 'head'", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mAttributeError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[11]\u001b[39m\u001b[32m, line 4\u001b[39m\n\u001b[32m 2\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mTotal papers in database: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(arxiv_papers_df)\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n\u001b[32m 3\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[33mFirst 5 papers:\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m----> \u001b[39m\u001b[32m4\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[43marxiv_papers_df\u001b[49m\u001b[43m.\u001b[49m\u001b[43mhead\u001b[49m())\n", - "\u001b[31mAttributeError\u001b[39m: 'list' object has no attribute 'head'" + "First 5 papers:\n", + "[(2, '1903.03894v4', '1903.03894', '4', 'GNNExplainer: Generating Explanations for Graph Neural Networks', \"Graph Neural Networks (GNNs) are a powerful tool for machine learning on\\ngraphs.GNNs combine node feature information with the graph structure by\\nrecursively passing neural messages along edges of the input graph. However,\\nincorporating both graph structure and feature information leads to complex\\nmodels, and explaining predictions made by GNNs remains unsolved. Here we\\npropose GNNExplainer, the first general, model-agnostic approach for providing\\ninterpretable explanations for predictions of any GNN-based model on any\\ngraph-based machine learning task. Given an instance, GNNExplainer identifies a\\ncompact subgraph structure and a small subset of node features that have a\\ncrucial role in GNN's prediction. Further, GNNExplainer can generate consistent\\nand concise explanations for an entire class of instances. We formulate\\nGNNExplainer as an optimization task that maximizes the mutual information\\nbetween a GNN's prediction and distribution of possible subgraph structures.\\nExperiments on synthetic and real-world graphs show that our approach can\\nidentify important graph structures as well as node features, and outperforms\\nbaselines by 17.1% on average. GNNExplainer provides a variety of benefits,\\nfrom the ability to visualize semantically relevant structures to\\ninterpretability, to giving insights into errors of faulty GNNs.\", datetime.datetime(2019, 3, 10, 0, 56, 26), {'id': '1903.03894v4', 'url': 'http://arxiv.org/abs/1903.03894v4', 'title': 'GNNExplainer: Generating Explanations for Graph Neural Networks', 'authors': ['Rex Ying', 'Dylan Bourgeois', 'Jiaxuan You', 'Marinka Zitnik', 'Jure Leskovec'], 'abstract': \"Graph Neural Networks (GNNs) are a powerful tool for machine learning on\\ngraphs.GNNs combine node feature information with the graph structure by\\nrecursively passing neural messages along edges of the input graph. However,\\nincorporating both graph structure and feature information leads to complex\\nmodels, and explaining predictions made by GNNs remains unsolved. Here we\\npropose GNNExplainer, the first general, model-agnostic approach for providing\\ninterpretable explanations for predictions of any GNN-based model on any\\ngraph-based machine learning task. Given an instance, GNNExplainer identifies a\\ncompact subgraph structure and a small subset of node features that have a\\ncrucial role in GNN's prediction. Further, GNNExplainer can generate consistent\\nand concise explanations for an entire class of instances. We formulate\\nGNNExplainer as an optimization task that maximizes the mutual information\\nbetween a GNN's prediction and distribution of possible subgraph structures.\\nExperiments on synthetic and real-world graphs show that our approach can\\nidentify important graph structures as well as node features, and outperforms\\nbaselines by 17.1% on average. GNNExplainer provides a variety of benefits,\\nfrom the ability to visualize semantically relevant structures to\\ninterpretability, to giving insights into errors of faulty GNNs.\", 'published': '2019-03-10 00:56:26+00:00', 'categories': ['cs.LG', 'stat.ML']}), (5, '1706.03762v7', '1706.03762', '7', 'Attention Is All You Need', 'The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.', datetime.datetime(2017, 6, 12, 0, 0), {'venue': 'NeurIPS 2017', 'pdf_url': 'https://arxiv.org/pdf/1706.03762.pdf'}), (6, '1810.04805v2', '1810.04805', '2', 'BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding', 'We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications.', datetime.datetime(2018, 10, 11, 0, 0), {'venue': 'NAACL 2019', 'citations': 50000})]\n" ] } ], @@ -321,16 +311,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "get-paper-by-id", "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Paper details:\n", - "{'id': 2, 'arxiv_id': '1810.04805v2', 'base_arxiv_id': 1810.04805, 'version': 2, 'title': 'BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding', 'abstract': 'We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers.', 'submit_date': '2018-10-11', 'metadata': '{\"venue\": \"NAACL 2019\", \"citations\": 60000}'}\n" + "ename": "AttributeError", + "evalue": "'SQLArxivPapers' object has no attribute 'get_paper_by_id'", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mAttributeError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[9]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m 1\u001b[39m paper_id = {\u001b[33m\"\u001b[39m\u001b[33marxiv_id\u001b[39m\u001b[33m\"\u001b[39m: \u001b[33m\"\u001b[39m\u001b[33m1810.04805v2\u001b[39m\u001b[33m\"\u001b[39m}\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m paper_features = \u001b[43mresearch_arcade\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget_node_features_by_id\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43marxiv_papers\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpaper_id\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 3\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33mPaper details:\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 4\u001b[39m \u001b[38;5;28mprint\u001b[39m(paper_features.to_dict(orient=\u001b[33m\"\u001b[39m\u001b[33mrecords\u001b[39m\u001b[33m\"\u001b[39m)[\u001b[32m0\u001b[39m])\n", + "\u001b[36mFile \u001b[39m\u001b[32m/data/cl195/paper-crawler/research_arcade/research_arcade.py:224\u001b[39m, in \u001b[36mResearchArcade.get_node_features_by_id\u001b[39m\u001b[34m(self, table, primary_key)\u001b[39m\n\u001b[32m 222\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.arxiv_tables.get_table_by_id(**primary_key)\n\u001b[32m 223\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m table == \u001b[33m'\u001b[39m\u001b[33marxiv_papers\u001b[39m\u001b[33m'\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m224\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43marxiv_papers\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget_paper_by_id\u001b[49m(**primary_key)\n\u001b[32m 225\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m table == \u001b[33m'\u001b[39m\u001b[33marxiv_paragraphs\u001b[39m\u001b[33m'\u001b[39m:\n\u001b[32m 226\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.arxiv_paragraphs.get_paragraph_by_id(**primary_key)\n", + "\u001b[31mAttributeError\u001b[39m: 'SQLArxivPapers' object has no attribute 'get_paper_by_id'" ] } ], @@ -351,7 +345,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "update-paper", "metadata": {}, "outputs": [ @@ -389,16 +383,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "delete-paper", "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Deleted paper:\n", - "True\n" + "ename": "AttributeError", + "evalue": "'SQLArxivPapers' object has no attribute 'delete_paper_by_id'", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mAttributeError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[11]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;66;03m# Delete a paper by ID\u001b[39;00m\n\u001b[32m 2\u001b[39m paper_id = {\u001b[33m\"\u001b[39m\u001b[33marxiv_id\u001b[39m\u001b[33m\"\u001b[39m: \u001b[33m\"\u001b[39m\u001b[33m1706.03762v7\u001b[39m\u001b[33m\"\u001b[39m}\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m deleted_paper = \u001b[43mresearch_arcade\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdelete_node_by_id\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43marxiv_papers\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpaper_id\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 4\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33mDeleted paper:\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 5\u001b[39m \u001b[38;5;28mprint\u001b[39m(deleted_paper)\n", + "\u001b[36mFile \u001b[39m\u001b[32m/data/cl195/paper-crawler/research_arcade/research_arcade.py:163\u001b[39m, in \u001b[36mResearchArcade.delete_node_by_id\u001b[39m\u001b[34m(self, table, primary_key)\u001b[39m\n\u001b[32m 161\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.arxiv_tables.delete_table_by_id(**primary_key)\n\u001b[32m 162\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m table == \u001b[33m'\u001b[39m\u001b[33marxiv_papers\u001b[39m\u001b[33m'\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m163\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43marxiv_papers\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdelete_paper_by_id\u001b[49m(**primary_key)\n\u001b[32m 164\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m table == \u001b[33m'\u001b[39m\u001b[33marxiv_paragraphs\u001b[39m\u001b[33m'\u001b[39m:\n\u001b[32m 165\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.arxiv_paragraphs.delete_paragraph_by_id(**primary_key)\n", + "\u001b[31mAttributeError\u001b[39m: 'SQLArxivPapers' object has no attribute 'delete_paper_by_id'" ] } ], @@ -452,14 +450,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Successfully imported 10 authors from ./examples/csv_data/csv_arxiv_authors_example.csv\n" + "Error: CSV file ./examples/csv_data/csv_arxiv_authors_example.csv does not exist.\n" ] } ], @@ -477,14 +475,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "No new authors to import (all authors already exist)\n" + "Error: JSON file ./examples/json_data/json_arxiv_authors_example.json does not exist.\n" ] } ], @@ -503,7 +501,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "insert-authors", "metadata": {}, "outputs": [ @@ -564,7 +562,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "get-all-authors", "metadata": {}, "outputs": [ @@ -572,122 +570,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "Total authors in database: 55\n", + "Total authors in database: 14391\n", "\n", "All authors:\n", - " id semantic_scholar_id name \\\n", - "0 1 1234567 Ashish Vaswani \n", - "1 2 2345678 Noam Shazeer \n", - "2 3 3456789 Niki Parmar \n", - "3 4 4567890 Jakob Uszkoreit \n", - "4 5 5678901 Llion Jones \n", - "5 6 6789012 Aidan N. Gomez \n", - "6 7 7890123 Lukasz Kaiser \n", - "7 8 8901234 Illia Polosukhin \n", - "8 9 9012345 Jacob Devlin \n", - "9 10 1234098 Ming-Wei Chang \n", - "10 11 1234567 Ashish Vaswani \n", - "11 12 2345678 Noam Shazeer \n", - "12 13 3456789 Niki Parmar \n", - "13 14 4567890 Jakob Uszkoreit \n", - "14 15 5678901 Llion Jones \n", - "15 16 6789012 Aidan N. Gomez \n", - "16 17 7890123 Lukasz Kaiser \n", - "17 18 8901234 Illia Polosukhin \n", - "18 19 9012345 Jacob Devlin \n", - "19 20 1234098 Ming-Wei Chang \n", - "20 21 1234567 Ashish Vaswani \n", - "21 22 2345678 Noam Shazeer \n", - "22 23 3456789 Niki Parmar \n", - "23 24 4567890 Jakob Uszkoreit \n", - "24 25 5678901 Llion Jones \n", - "25 26 6789012 Aidan N. 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S. Khodaei', 'https://www.semanticscholar.org/author/21264055'), (14968, '153622034', 'M. H. Aliabadi', 'https://www.semanticscholar.org/author/153622034'), (14969, '2321664693', 'Xiaojie Li', 'https://www.semanticscholar.org/author/2321664693'), (14970, '2236881361', 'Zhijie Cai', 'https://www.semanticscholar.org/author/2236881361'), (14971, '2372746703', 'Nan Qi', 'https://www.semanticscholar.org/author/2372746703'), (14972, '2376063623', 'Chao Dong', 'https://www.semanticscholar.org/author/2376063623'), (14973, '2346063915', 'Guangxu Zhu', 'https://www.semanticscholar.org/author/2346063915'), (14974, '2374125858', 'Haixia Ma', 'https://www.semanticscholar.org/author/2374125858'), (14976, '2346995791', 'Shi Jin', 'https://www.semanticscholar.org/author/2346995791'), (14977, '2373333793', 'Sebastian Barros Elgueta', 'https://www.semanticscholar.org/author/2373333793'), (14978, '23218541', 'P. M. U. 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Shashua', 'https://www.semanticscholar.org/author/3140335'), (14997, '2373603330', 'Zhaoyang Guan', 'https://www.semanticscholar.org/author/2373603330'), (14998, '2351000034', 'Leyi Zhao', 'https://www.semanticscholar.org/author/2351000034'), (15000, '2375887351', 'Xinyu Ying', 'https://www.semanticscholar.org/author/2375887351'), (15001, '2374241409', 'Hanlin Zhang', 'https://www.semanticscholar.org/author/2374241409'), (15002, '2373605358', 'Michele Pak', 'https://www.semanticscholar.org/author/2373605358'), (15003, '2364955408', 'Yangfan He', 'https://www.semanticscholar.org/author/2364955408'), (15004, '2374195579', 'Yi Xin', 'https://www.semanticscholar.org/author/2374195579'), (15005, '2373748377', 'Jianhui Wang', 'https://www.semanticscholar.org/author/2373748377'), (15007, 'ss_ashish_vaswani', 'Ashish Vaswani', 'https://scholar.google.com/citations?user=oR9sCGYAAAAJ'), (15008, 'ss_noam_shazeer', 'Noam Shazeer', 'https://scholar.google.com/citations?user=oR9sCGYAAAAJ'), (15009, 'ss_niki_parmar', 'Niki Parmar', 'https://scholar.google.com/citations?user=oR9sCGYAAAAJ'), (15010, 'ss_jakob_uszkoreit', 'Jakob Uszkoreit', 'https://scholar.google.com/citations?user=oR9sCGYAAAAJ'), (15011, 'ss_llion_jones', 'Llion Jones', 'https://scholar.google.com/citations?user=oR9sCGYAAAAJ')]\n" ] } ], @@ -713,16 +599,20 @@ "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Author details:\n", - "None\n" + "ename": "TypeError", + "evalue": "SQLArxivAuthors.get_author_by_id() got an unexpected keyword argument 'semantic_scholar_id'", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mTypeError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[20]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m 1\u001b[39m author_id = {\u001b[33m\"\u001b[39m\u001b[33msemantic_scholar_id\u001b[39m\u001b[33m\"\u001b[39m: \u001b[32m12288033664\u001b[39m}\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m author_features = \u001b[43mresearch_arcade\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget_node_features_by_id\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43marxiv_authors\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mauthor_id\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 3\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33mAuthor details:\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 4\u001b[39m \u001b[38;5;28mprint\u001b[39m(author_features)\n", + "\u001b[36mFile \u001b[39m\u001b[32m/data/cl195/paper-crawler/research_arcade/research_arcade.py:216\u001b[39m, in \u001b[36mResearchArcade.get_node_features_by_id\u001b[39m\u001b[34m(self, table, primary_key)\u001b[39m\n\u001b[32m 214\u001b[39m \u001b[38;5;66;03m# Tables in arxiv dataset\u001b[39;00m\n\u001b[32m 215\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m table == \u001b[33m'\u001b[39m\u001b[33marxiv_authors\u001b[39m\u001b[33m'\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m216\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43marxiv_authors\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget_author_by_id\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mprimary_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 217\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m table == \u001b[33m'\u001b[39m\u001b[33marxiv_categories\u001b[39m\u001b[33m'\u001b[39m:\n\u001b[32m 218\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.arxiv_categories.get_category_by_id(**primary_key)\n", + "\u001b[31mTypeError\u001b[39m: SQLArxivAuthors.get_author_by_id() got an unexpected keyword argument 'semantic_scholar_id'" ] } ], "source": [ - "author_id = {\"semantic_scholar_id\": 8901234}\n", + "author_id = {\"semantic_scholar_id\": 2288033664}\n", "author_features = research_arcade.get_node_features_by_id(\"arxiv_authors\", author_id)\n", "print(\"Author details:\")\n", "print(author_features)" @@ -738,15 +628,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "update-author", "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Author updated successfully!\n" + "ename": "TypeError", + "evalue": "SQLArxivAuthors.update_author() missing 1 required positional argument: 'id'", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mTypeError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[19]\u001b[39m\u001b[32m, line 6\u001b[39m\n\u001b[32m 1\u001b[39m updated_author = {\n\u001b[32m 2\u001b[39m \u001b[33m'\u001b[39m\u001b[33msemantic_scholar_id\u001b[39m\u001b[33m'\u001b[39m: \u001b[33m'\u001b[39m\u001b[33mss_ashish_vaswani\u001b[39m\u001b[33m'\u001b[39m,\n\u001b[32m 3\u001b[39m \u001b[33m'\u001b[39m\u001b[33mhomepage\u001b[39m\u001b[33m'\u001b[39m: \u001b[33m'\u001b[39m\u001b[33mhttps://ashishvaswani.com\u001b[39m\u001b[33m'\u001b[39m\n\u001b[32m 4\u001b[39m }\n\u001b[32m----> \u001b[39m\u001b[32m6\u001b[39m \u001b[43mresearch_arcade\u001b[49m\u001b[43m.\u001b[49m\u001b[43mupdate_node\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43marxiv_authors\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnode_features\u001b[49m\u001b[43m=\u001b[49m\u001b[43mupdated_author\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 7\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33mAuthor updated successfully!\u001b[39m\u001b[33m\"\u001b[39m)\n", + "\u001b[36mFile \u001b[39m\u001b[32m/data/cl195/paper-crawler/research_arcade/research_arcade.py:185\u001b[39m, in \u001b[36mResearchArcade.update_node\u001b[39m\u001b[34m(self, table, node_features)\u001b[39m\n\u001b[32m 183\u001b[39m \u001b[38;5;66;03m# Tables in arxiv dataset\u001b[39;00m\n\u001b[32m 184\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m table == \u001b[33m'\u001b[39m\u001b[33marxiv_authors\u001b[39m\u001b[33m'\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m185\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43marxiv_authors\u001b[49m\u001b[43m.\u001b[49m\u001b[43mupdate_author\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mnode_features\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 186\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m table == \u001b[33m'\u001b[39m\u001b[33marxiv_categories\u001b[39m\u001b[33m'\u001b[39m:\n\u001b[32m 187\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.arxiv_categories.update_category(**node_features)\n", + "\u001b[31mTypeError\u001b[39m: SQLArxivAuthors.update_author() missing 1 required positional argument: 'id'" ] } ], @@ -783,7 +678,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "id": "168633f2", "metadata": {}, "outputs": [ @@ -791,6 +686,8 @@ "name": "stdout", "output_type": "stream", "text": [ + "./download/1806.08804v4/1806.08804v4.tar.gz\n", + "paper with id 1806.08804v4 downloaded\n", "{'id': '1903.03894v4', 'title': 'GNNExplainer: Generating Explanations for Graph Neural Networks', 'abstract': \"Graph Neural Networks (GNNs) are a powerful tool for machine learning on\\ngraphs.GNNs combine node feature information with the graph structure by\\nrecursively passing neural messages along edges of the input graph. However,\\nincorporating both graph structure and feature information leads to complex\\nmodels, and explaining predictions made by GNNs remains unsolved. Here we\\npropose GNNExplainer, the first general, model-agnostic approach for providing\\ninterpretable explanations for predictions of any GNN-based model on any\\ngraph-based machine learning task. Given an instance, GNNExplainer identifies a\\ncompact subgraph structure and a small subset of node features that have a\\ncrucial role in GNN's prediction. Further, GNNExplainer can generate consistent\\nand concise explanations for an entire class of instances. We formulate\\nGNNExplainer as an optimization task that maximizes the mutual information\\nbetween a GNN's prediction and distribution of possible subgraph structures.\\nExperiments on synthetic and real-world graphs show that our approach can\\nidentify important graph structures as well as node features, and outperforms\\nbaselines by 17.1% on average. GNNExplainer provides a variety of benefits,\\nfrom the ability to visualize semantically relevant structures to\\ninterpretability, to giving insights into errors of faulty GNNs.\", 'authors': ['Rex Ying', 'Dylan Bourgeois', 'Jiaxuan You', 'Marinka Zitnik', 'Jure Leskovec'], 'published': '2019-03-10 00:56:26+00:00', 'categories': ['cs.LG', 'stat.ML'], 'url': 'http://arxiv.org/abs/1903.03894v4'}\n", "{'id': '1806.08804v4', 'title': 'Hierarchical Graph Representation Learning with Differentiable Pooling', 'abstract': 'Recently, graph neural networks (GNNs) have revolutionized the field of graph\\nrepresentation learning through effectively learned node embeddings, and\\nachieved state-of-the-art results in tasks such as node classification and link\\nprediction. However, current GNN methods are inherently flat and do not learn\\nhierarchical representations of graphs---a limitation that is especially\\nproblematic for the task of graph classification, where the goal is to predict\\nthe label associated with an entire graph. Here we propose DiffPool, a\\ndifferentiable graph pooling module that can generate hierarchical\\nrepresentations of graphs and can be combined with various graph neural network\\narchitectures in an end-to-end fashion. DiffPool learns a differentiable soft\\ncluster assignment for nodes at each layer of a deep GNN, mapping nodes to a\\nset of clusters, which then form the coarsened input for the next GNN layer.\\nOur experimental results show that combining existing GNN methods with DiffPool\\nyields an average improvement of 5-10% accuracy on graph classification\\nbenchmarks, compared to all existing pooling approaches, achieving a new\\nstate-of-the-art on four out of five benchmark data sets.', 'authors': ['Rex Ying', 'Jiaxuan You', 'Christopher Morris', 'Xiang Ren', 'William L. Hamilton', 'Jure Leskovec'], 'published': '2018-06-22 18:04:46+00:00', 'categories': ['cs.LG', 'cs.NE', 'cs.SI', 'stat.ML'], 'url': 'http://arxiv.org/abs/1806.08804v4'}\n" ] @@ -810,14 +707,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "No new categories to import (all categories already exist)\n" + "Error: CSV file ./examples/csv_data/csv_arxiv_categories_example.csv does not exist.\n" ] } ], @@ -835,14 +732,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "No new categories to import (all categories already exist)\n" + "Error: JSON file ./examples/json_data/json_arxiv_categories_example.json does not exist.\n" ] } ], @@ -861,7 +758,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "id": "insert-categories", "metadata": {}, "outputs": [ @@ -916,7 +813,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "id": "get-all-categories", "metadata": {}, "outputs": [ @@ -924,21 +821,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "Total categories: 11\n", + "Total categories: 7\n", "\n", "All categories:\n", - " id name description\n", - "0 1 cs.LG NaN\n", - "1 2 stat.ML NaN\n", - "2 3 cs.NE NaN\n", - "3 4 cs.SI NaN\n", - "4 5 cs.AI Artificial Intelligence\n", - "5 7 cs.CL Computation and Language\n", - "6 8 cs.CV Computer Vision and Pattern Recognition\n", - "7 11 cs.CR Cryptography and Security\n", - "8 12 cs.DS Data Structures and Algorithms\n", - "9 13 cs.IT Information Theory\n", - "10 14 math.IT Information Theory (Math)\n" + "[(1, 'cs.LG', None), (2, 'stat.ML', None), (4, 'cs.NE', None), (5, 'cs.SI', None), (7, 'cs.CL', 'Computation and Language (Natural Language Processing)'), (9, 'cs.AI', 'Artificial Intelligence'), (10, 'cs.CV', 'Computer Vision and Pattern Recognition')]\n" ] } ], @@ -975,17 +861,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "id": "insert-figures", "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Inserted Figure 1\n", - "Inserted Figure 2\n", - "Inserted Figure 3\n" + "ename": "UndefinedObject", + "evalue": "constraint \"ux_arxiv_figures_name_notnull\" for table \"arxiv_figures\" does not exist\n", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mUndefinedObject\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[26]\u001b[39m\u001b[32m, line 27\u001b[39m\n\u001b[32m 2\u001b[39m figures = [\n\u001b[32m 3\u001b[39m {\n\u001b[32m 4\u001b[39m \u001b[33m'\u001b[39m\u001b[33mpaper_arxiv_id\u001b[39m\u001b[33m'\u001b[39m: \u001b[33m'\u001b[39m\u001b[33m1706.03762v7\u001b[39m\u001b[33m'\u001b[39m,\n\u001b[32m (...)\u001b[39m\u001b[32m 23\u001b[39m }\n\u001b[32m 24\u001b[39m ]\n\u001b[32m 26\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m figure \u001b[38;5;129;01min\u001b[39;00m figures:\n\u001b[32m---> \u001b[39m\u001b[32m27\u001b[39m \u001b[43mresearch_arcade\u001b[49m\u001b[43m.\u001b[49m\u001b[43minsert_node\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43marxiv_figures\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnode_features\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfigure\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 28\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mInserted \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfigure[\u001b[33m'\u001b[39m\u001b[33mname\u001b[39m\u001b[33m'\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n", + "\u001b[36mFile \u001b[39m\u001b[32m/data/cl195/paper-crawler/research_arcade/research_arcade.py:128\u001b[39m, in \u001b[36mResearchArcade.insert_node\u001b[39m\u001b[34m(self, table, node_features)\u001b[39m\n\u001b[32m 126\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.arxiv_categories.insert_category(**node_features)\n\u001b[32m 127\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m table == \u001b[33m'\u001b[39m\u001b[33marxiv_figures\u001b[39m\u001b[33m'\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m128\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43marxiv_figures\u001b[49m\u001b[43m.\u001b[49m\u001b[43minsert_figure\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mnode_features\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 129\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m table == \u001b[33m'\u001b[39m\u001b[33marxiv_tables\u001b[39m\u001b[33m'\u001b[39m:\n\u001b[32m 130\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.arxiv_tables.insert_table(**node_features)\n", + "\u001b[36mFile \u001b[39m\u001b[32m/data/cl195/paper-crawler/research_arcade/sql_database/sql_arxiv_figures.py:80\u001b[39m, in \u001b[36mSQLArxivFigure.insert_figure\u001b[39m\u001b[34m(self, paper_arxiv_id, path, caption, label, name)\u001b[39m\n\u001b[32m 78\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m 79\u001b[39m cur = conn.cursor()\n\u001b[32m---> \u001b[39m\u001b[32m80\u001b[39m \u001b[43mcur\u001b[49m\u001b[43m.\u001b[49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 81\u001b[39m \u001b[38;5;250;43m \u001b[39;49m\u001b[33;43;03m\"\"\"\u001b[39;49;00m\n\u001b[32m 82\u001b[39m \u001b[33;43;03m INSERT INTO arxiv_figures (paper_arxiv_id, path, caption, label, name)\u001b[39;49;00m\n\u001b[32m 83\u001b[39m \u001b[33;43;03m VALUES (%s, %s, %s, %s, %s)\u001b[39;49;00m\n\u001b[32m 84\u001b[39m \u001b[33;43;03m ON CONFLICT ON CONSTRAINT ux_arxiv_figures_name_notnull DO NOTHING\u001b[39;49;00m\n\u001b[32m 85\u001b[39m \u001b[33;43;03m RETURNING id\u001b[39;49;00m\n\u001b[32m 86\u001b[39m \u001b[33;43;03m \"\"\"\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m 87\u001b[39m \u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mpaper_arxiv_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcaption\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 88\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 89\u001b[39m res = cur.fetchone()\n\u001b[32m 90\u001b[39m cur.close()\n", + "\u001b[31mUndefinedObject\u001b[39m: constraint \"ux_arxiv_figures_name_notnull\" for table \"arxiv_figures\" does not exist\n" ] } ], @@ -1030,42 +920,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "id": "get-all-figures", "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total figures: 11\n", - "\n", - "All figures:\n", - " name caption \\\n", - "0 Figure 1 The Transformer model architecture. The left s... \n", - "1 Figure 2 Scaled Dot-Product Attention and Multi-Head At... \n", - "2 Figure 3 Positional encoding visualization showing sine... \n", - "3 figure1 The Transformer model architecture \n", - "4 figure2 Multi-head attention mechanism visualization \n", - "5 figure3 Variations on the Transformer architecture \n", - "6 figure4 BERT model architecture and pre-training tasks \n", - "7 figure5 Fine-tuning BERT for different tasks \n", - "8 figure6 Residual learning: a building block \n", - "9 figure7 ResNet architectures for ImageNet \n", - "10 figure8 Vision Transformer (ViT) model overview \n", - "\n", - " label \n", - "0 fig:architecture \n", - "1 fig:attention \n", - "2 fig:positional \n", - "3 fig:architecture \n", - "4 fig:attention \n", - "5 fig:variations \n", - "6 fig:bert_arch \n", - "7 fig:fine_tune \n", - "8 fig:residual \n", - "9 fig:resnet_arch \n", - "10 fig:vit \n" + "ename": "TypeError", + "evalue": "object of type 'NoneType' has no len()", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mTypeError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[27]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m 1\u001b[39m figures_df = research_arcade.get_all_node_features(\u001b[33m\"\u001b[39m\u001b[33marxiv_figures\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mTotal figures: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mfigures_df\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n\u001b[32m 3\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[33mAll figures:\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 4\u001b[39m \u001b[38;5;28mprint\u001b[39m(figures_df[[\u001b[33m'\u001b[39m\u001b[33mname\u001b[39m\u001b[33m'\u001b[39m, \u001b[33m'\u001b[39m\u001b[33mcaption\u001b[39m\u001b[33m'\u001b[39m, \u001b[33m'\u001b[39m\u001b[33mlabel\u001b[39m\u001b[33m'\u001b[39m]])\n", + "\u001b[31mTypeError\u001b[39m: object of type 'NoneType' has no len()" ] } ], @@ -1102,7 +969,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "id": "54a13d98", "metadata": {}, "outputs": [ @@ -1113,61 +980,38 @@ "seed: ['1903.03894v4']\n", "BFS_que.qsize(): 1\n", "current paper: 1903.03894v4\n", - "Thread 13079867392 Processing 1903.03894v4\n" + "Thread 136281040344640 Processing 1903.03894v4\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "x 000abstract.tex\n", - "x 010intro.tex\n", - "x 020related.tex\n", - "x 030background.tex\n", - "x 030formulation.tex\n", - "x 030proposed.tex\n", - "x 040experiments.tex\n", - "x 050conclusion.tex\n", - "x 060supplement.tex\n", - "x acmart.bib\n", - "x acmart.cls\n", - "x acmart.dtx\n", - "x acmart.ins\n", - "x ACM-Reference-Format.bbx\n", - "x ACM-Reference-Format.bst\n", - "x ACM-Reference-Format.cbx\n", - "x ACM-Reference-Format.dbx\n", - "x figs/\n", - "x figs/explainer-introduction_v2.pdf\n", - "x figs/explainer-motivation.pdf\n", - "x figs/explainer.pdf\n", - "x figs/feature_importance_v2.pdf\n", - "x figs/fig3-graph-cls-v2.pdf\n", - "x figs/fig3-graph-cls.pdf\n", - "x figs/fig3-node-cls-v3.pdf\n", - "x figs/fig3-node-cls.pdf\n", - "x figs/fig3-v4.pdf\n", - "x figs/fig3-v5.pdf\n", - "x figs/including-node-features.pdf\n", - "x figs/local_subgraph.png\n", - "x figs/motivation-node-features.pdf\n", - "x figs/prototype.png\n", - "x figs/prototype1.png\n", - "x figs/single-instance-explanation-final.pdf\n", - "x figs/single-instance-explanation2.pdf\n", - "x figs/single-instance-explanations.pdf: truncated gzip input\n", - "tar: Error exit delayed from previous errors.\n" + "\n", + "gzip: stdin: unexpected end of file\n", + "tar: Unexpected EOF in archive\n", + "tar: Unexpected EOF in archive\n", + "tar: Error is not recoverable: exiting now\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Thread 13079867392 Finished processing 1903.03894v4 (1/999999999) Time elapsed: 0.70s\n", + "Thread 136281040344640 Finished processing 1903.03894v4 (1/999999999) Time elapsed: 1.19s\n", "'NoneType' object is not subscriptable\n", - "Thread 13079867392 Failed to process 1903.03894v4\n", - "Thread 8614781504 Finished processing 1 papers\n", - "Error: The file at path './download/output/1903.03894v4.json' was not found.\n" + "Thread 136281040344640 Failed to process 1903.03894v4\n", + "Thread 136284945086272 Finished processing 1 papers\n", + "Error: The file './download/output/1903.03894v4.json' was not found.\n", + "seed: ['1806.08804v4']\n", + "BFS_que.qsize(): 1\n", + "current paper: 1806.08804v4\n", + "Thread 136281040344640 Processing 1806.08804v4\n", + "Thread 136281040344640 Processing file paper-diffpool.tex\n", + "numbmer of citations in node info method: 0\n", + "Cannot find the bib file refs.bib\n", + "Thread 136281040344640 Finished processing 1806.08804v4 (1/999999999) Time elapsed: 1.68s\n", + "Thread 136284945086272 Finished processing 1 papers\n" ] } ], @@ -1185,14 +1029,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Successfully imported 6 tables from ./examples/csv_data/csv_arxiv_tables_example.csv\n" + "Error: CSV file ./examples/csv_data/csv_arxiv_tables_example.csv does not exist.\n" ] } ], @@ -1211,7 +1055,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "id": "b809fdd9", "metadata": {}, "outputs": [ @@ -1266,7 +1110,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "id": "1f1357fc", "metadata": {}, "outputs": [ @@ -1274,21 +1118,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "Total categories: 11\n", + "Total categories: 7\n", "\n", "All categories:\n", - " id name description\n", - "0 1 cs.LG NaN\n", - "1 2 stat.ML NaN\n", - "2 3 cs.NE NaN\n", - "3 4 cs.SI NaN\n", - "4 5 cs.AI Artificial Intelligence\n", - "5 7 cs.CL Computation and Language\n", - "6 8 cs.CV Computer Vision and Pattern Recognition\n", - "7 11 cs.CR Cryptography and Security\n", - "8 12 cs.DS Data Structures and Algorithms\n", - "9 13 cs.IT Information Theory\n", - "10 14 math.IT Information Theory (Math)\n" + "[(1, 'cs.LG', None), (2, 'stat.ML', None), (4, 'cs.NE', None), (5, 'cs.SI', None), (7, 'cs.CL', 'Computation and Language (Natural Language Processing)'), (9, 'cs.AI', 'Artificial Intelligence'), (10, 'cs.CV', 'Computer Vision and Pattern Recognition')]\n" ] } ], @@ -1325,7 +1158,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "id": "195e218d", "metadata": {}, "outputs": [ @@ -1336,61 +1169,31 @@ "seed: ['1903.03894v4']\n", "BFS_que.qsize(): 1\n", "current paper: 1903.03894v4\n", - "Thread 13079867392 Processing 1903.03894v4\n" + "Thread 136281040344640 Processing 1903.03894v4\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "x 000abstract.tex\n", - "x 010intro.tex\n", - "x 020related.tex\n", - "x 030background.tex\n", - "x 030formulation.tex\n", - "x 030proposed.tex\n", - "x 040experiments.tex\n", - "x 050conclusion.tex\n", - "x 060supplement.tex\n", - "x acmart.bib\n", - "x acmart.cls\n", - "x acmart.dtx\n", - "x acmart.ins\n", - "x ACM-Reference-Format.bbx\n", - "x ACM-Reference-Format.bst\n", - "x ACM-Reference-Format.cbx\n", - "x ACM-Reference-Format.dbx\n", - "x figs/\n", - "x figs/explainer-introduction_v2.pdf\n", - "x figs/explainer-motivation.pdf\n", - "x figs/explainer.pdf\n", - "x figs/feature_importance_v2.pdf\n", - "x figs/fig3-graph-cls-v2.pdf\n", - "x figs/fig3-graph-cls.pdf\n", - "x figs/fig3-node-cls-v3.pdf\n", - "x figs/fig3-node-cls.pdf\n", - "x figs/fig3-v4.pdf\n", - "x figs/fig3-v5.pdf\n", - "x figs/including-node-features.pdf\n", - "x figs/local_subgraph.png\n", - "x figs/motivation-node-features.pdf\n", - "x figs/prototype.png\n", - "x figs/prototype1.png\n", - "x figs/single-instance-explanation-final.pdf\n", - "x figs/single-instance-explanation2.pdf\n", - "x figs/single-instance-explanations.pdf: truncated gzip input\n", - "tar: Error exit delayed from previous errors.\n" + "\n", + "gzip: stdin: unexpected end of file\n", + "tar: Unexpected EOF in archive\n", + "tar: Unexpected EOF in archive\n", + "tar: Error is not recoverable: exiting now\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Thread 13079867392 Finished processing 1903.03894v4 (1/999999999) Time elapsed: 0.71s\n", + "Thread 136281040344640 Finished processing 1903.03894v4 (1/999999999) Time elapsed: 1.04s\n", "'NoneType' object is not subscriptable\n", - "Thread 13079867392 Failed to process 1903.03894v4\n", - "Thread 8614781504 Finished processing 1 papers\n", - "Error: The file with path './download/output/1903.03894v4.json' was not found.\n" + "Thread 136281040344640 Failed to process 1903.03894v4\n", + "Thread 136284945086272 Finished processing 1 papers\n", + "Error: The file './download/output/1903.03894v4.json' was not found.\n", + "An unexpected error occurred: constraint \"ux_arxiv_figures_name_notnull\" for table \"arxiv_figures\" does not exist\n", + "\n" ] } ], @@ -1459,7 +1262,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "id": "insert-tables", "metadata": {}, "outputs": [ @@ -1514,7 +1317,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "id": "get-all-tables", "metadata": {}, "outputs": [ @@ -1522,44 +1325,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "Total tables: 34\n", + "Total tables: 5\n", "\n", "All tables:\n", - " label caption\n", - "0 tab:wmt_results Machine translation performance on WMT datasets\n", - "1 tab:variations Variations on the Transformer architecture\n", - "2 tab:glue BERT performance on GLUE benchmark tasks\n", - "3 tab:squad Results on SQuAD question answering\n", - "4 tab:imagenet Classification error on ImageNet validation set\n", - "5 tab:vit_perf Vision Transformer performance comparison\n", - "6 tab:wmt_results Machine translation performance on WMT datasets\n", - "7 tab:variations Variations on the Transformer architecture\n", - "8 tab:glue BERT performance on GLUE benchmark tasks\n", - "9 tab:squad Results on SQuAD question answering\n", - "10 tab:imagenet Classification error on ImageNet validation set\n", - "11 tab:vit_perf Vision Transformer performance comparison\n", - "12 tab:variations Variations on the Transformer architecture wit...\n", - "13 tab:wmt Performance of the Transformer on WMT 2014 Eng...\n", - "14 tab:parsing English constituency parsing results on WSJ te...\n", - "15 \\label{tab:results} \\caption{Classification accuracies in percent....\n", - "16 \\label{tab:results2} \\caption{Accuracy results of applying \\name to...\n", - "17 tab:wmt_results Machine translation performance on WMT datasets\n", - "18 tab:variations Variations on the Transformer architecture\n", - "19 tab:glue BERT performance on GLUE benchmark tasks\n", - "20 tab:squad Results on SQuAD question answering\n", - "21 tab:imagenet Classification error on ImageNet validation set\n", - "22 tab:vit_perf Vision Transformer performance comparison\n", - "23 \\label{tab:results} \\caption{Classification accuracies in percent....\n", - "24 \\label{tab:results2} \\caption{Accuracy results of applying \\name to...\n", - "25 tab:wmt_results Machine translation performance on WMT datasets\n", - "26 tab:variations Variations on the Transformer architecture\n", - "27 tab:glue BERT performance on GLUE benchmark tasks\n", - "28 tab:squad Results on SQuAD question answering\n", - "29 tab:imagenet Classification error on ImageNet validation set\n", - "30 tab:vit_perf Vision Transformer performance comparison\n", - "31 tab:variations Variations on the Transformer architecture wit...\n", - "32 tab:wmt Performance of the Transformer on WMT 2014 Eng...\n", - "33 tab:parsing English constituency parsing results on WSJ te...\n" + "[(1, '1806.08804v4', None, '\\\\caption{Classification accuracies in percent. The far-right column gives the relative increase in accuracy compared to the baseline \\\\textsc{GraphSage} approach.}', '\\\\label{tab:results}', '\\\\begin{tabular}{@{}clcccccc@{}}\\\\cmidrule[\\\\heavyrulewidth]{2-8}\\n& \\\\multirow{3}{*}{\\\\vspace*{8pt}\\\\textbf{Method}}&\\\\multicolumn{5}{c}{\\\\textbf{Data Set}}\\\\\\\\\\\\cmidrule{3-8}\\n& & {\\\\textsc{Enzymes}} & {\\\\textsc{D\\\\&D}} & {\\\\textsc{Reddit-Multi-12k}} & {\\\\textsc{Collab}} & {\\\\textsc{Proteins}} & {\\\\text{Gain}}\\n\\\\\\\\ \\\\cmidrule{2-8}\\n\\\\multirow{4}{*}{\\\\rotatebox{90}{\\\\hspace*{-6pt}Kernel}} \\n& \\\\textsc{Graphlet} & 41.03 & 74.85 & 21.73 & 64.66 & 72.91 & \\\\\\\\ \\n& \\\\textsc{Shortest-path} & 42.32 & 78.86 & 36.93 & 59.10 & 76.43 & \\\\\\\\ \\n& \\\\text{1-WL} & 53.43 & 74.02 & 39.03 & 78.61 & 73.76 & \\\\\\\\ \\n& \\\\text{WL-OA} & 60.13 & 79.04\\t & 44.38 & 80.74 & 75.26 & \\\\\\\\ \\\\cmidrule{2-8}\\n% GNN\\n& \\\\textsc{PatchySan} & -- & 76.27\\t & 41.32 & 72.60 & 75.00 & 4.17 \\\\\\\\ \\n\\\\multirow{7}{*}{\\\\rotatebox{90}{GNN}} \\n& \\\\textsc{GraphSage} & 54.25 & 75.42 \\t & 42.24 & 68.25 & 70.48 & --\\\\\\\\ \\n& \\\\textsc{ECC} & 53.50 & 74.10 & 41.73 & 67.79 & 72.65 & 0.11 \\\\\\\\\\t\\n& \\\\textsc{Set2set} & 60.15 & 78.12 & 43.49 & 71.75 & 74.29 & 3.32 \\\\\\\\ \\n& \\\\textsc{SortPool} & 57.12 & 79.37 & 41.82 & 73.76 & 75.54 & 3.39 \\\\\\\\ \\n& \\\\textsc{\\\\name-Det} & 58.33 & 75.47 & 46.18 & \\\\textbf{82.13} & 75.62 & 5.42 \\\\\\\\ \\n& \\\\textsc{\\\\name-NoLP} & 61.95 & 79.98\\t & 46.65 & 75.58 & 76.22 & 5.95 \\\\\\\\ \\n& \\\\textsc{\\\\name} & \\\\textbf{62.53} & \\\\textbf{80.64}\\t & \\\\textbf{47.08} & 75.48 & \\\\textbf{76.25} & \\\\textbf{6.27}\\\\\\\\ \\n\\\\cmidrule[\\\\heavyrulewidth]{2-8}\\n\\\\end{tabular}'), (2, '1806.08804v4', None, '\\\\caption{Accuracy results of applying \\\\name to \\\\textsc{S2V}.}', '\\\\label{tab:results2}', '\\\\begin{tabular}{@{}clccc@{}}\\\\cmidrule[\\\\heavyrulewidth]{2-5}\\n& \\\\multirow{3}{*}{\\\\vspace*{8pt}\\\\textbf{Data Set}}&\\\\multicolumn{3}{c}{\\\\textbf{Method}}\\\\\\\\\\\\cmidrule{3-5}\\n& & {\\\\textsc{S2V}} & {\\\\textsc{S2V with 1 DiffPool}} & {\\\\textsc{S2V with 2 DiffPool}}\\n\\\\\\\\ \\\\cmidrule{2-5}\\n& \\\\textsc{Enzymes} & \\t61.10 & 62.86 & \\\\textbf{63.33} \\\\\\\\ \\n& \\\\textsc{D\\\\&D} & 78.92 & 80.75 & \\\\textbf{82.07} \\\\\\\\ \\n\\\\cmidrule[\\\\heavyrulewidth]{2-5}\\n\\\\end{tabular}'), (3, '1706.03762v7', '/tables/model_variations.tex', 'Variations on the Transformer architecture with different hyperparameters.', 'tab:variations', 'Model | N | d_model | d_ff | h | d_k | d_v | P_drop | train time\\nbase | 6 | 512 | 2048 | 8 | 64 | 64 | 0.1 | 12 hrs'), (4, '1706.03762v7', '/tables/wmt_results.tex', 'Performance of the Transformer on WMT 2014 English-German and English-French translation tasks.', 'tab:wmt', 'Model | EN-DE BLEU | EN-FR BLEU\\nTransformer (base) | 27.3 | 38.1\\nTransformer (big) | 28.4 | 41.8'), (5, '1706.03762v7', '/tables/parsing_results.tex', 'English constituency parsing results on WSJ test set.', 'tab:parsing', 'Model | WSJ 23 F1\\nTransformer | 91.3')]\n" ] } ], @@ -1567,7 +1336,7 @@ "tables_df = research_arcade.get_all_node_features(\"arxiv_tables\")\n", "print(f\"Total tables: {len(tables_df)}\")\n", "print(\"\\nAll tables:\")\n", - "print(tables_df[['label', 'caption']])" + "print(tables_df)" ] }, { @@ -1596,7 +1365,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "id": "c1735fc6", "metadata": {}, "outputs": [ @@ -1607,61 +1376,29 @@ "seed: ['1903.03894v4']\n", "BFS_que.qsize(): 1\n", "current paper: 1903.03894v4\n", - "Thread 13079867392 Processing 1903.03894v4\n" + "Thread 136281040344640 Processing 1903.03894v4\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "x 000abstract.tex\n", - "x 010intro.tex\n", - "x 020related.tex\n", - "x 030background.tex\n", - "x 030formulation.tex\n", - "x 030proposed.tex\n", - "x 040experiments.tex\n", - "x 050conclusion.tex\n", - "x 060supplement.tex\n", - "x acmart.bib\n", - "x acmart.cls\n", - "x acmart.dtx\n", - "x acmart.ins\n", - "x ACM-Reference-Format.bbx\n", - "x ACM-Reference-Format.bst\n", - "x ACM-Reference-Format.cbx\n", - "x ACM-Reference-Format.dbx\n", - "x figs/\n", - "x figs/explainer-introduction_v2.pdf\n", - "x figs/explainer-motivation.pdf\n", - "x figs/explainer.pdf\n", - "x figs/feature_importance_v2.pdf\n", - "x figs/fig3-graph-cls-v2.pdf\n", - "x figs/fig3-graph-cls.pdf\n", - "x figs/fig3-node-cls-v3.pdf\n", - "x figs/fig3-node-cls.pdf\n", - "x figs/fig3-v4.pdf\n", - "x figs/fig3-v5.pdf\n", - "x figs/including-node-features.pdf\n", - "x figs/local_subgraph.png\n", - "x figs/motivation-node-features.pdf\n", - "x figs/prototype.png\n", - "x figs/prototype1.png\n", - "x figs/single-instance-explanation-final.pdf\n", - "x figs/single-instance-explanation2.pdf\n", - "x figs/single-instance-explanations.pdf: truncated gzip input\n", - "tar: Error exit delayed from previous errors.\n" + "\n", + "gzip: stdin: unexpected end of file\n", + "tar: Unexpected EOF in archive\n", + "tar: Unexpected EOF in archive\n", + "tar: Error is not recoverable: exiting now\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Thread 13079867392 Finished processing 1903.03894v4 (1/999999999) Time elapsed: 0.65s\n", + "Thread 136281040344640 Finished processing 1903.03894v4 (1/999999999) Time elapsed: 1.04s\n", "'NoneType' object is not subscriptable\n", - "Thread 13079867392 Failed to process 1903.03894v4\n", - "Thread 8614781504 Finished processing 1 papers\n", - "Error: The file at path './download/output/1903.03894v4.json' was not found.\n" + "Thread 136281040344640 Failed to process 1903.03894v4\n", + "Thread 136284945086272 Finished processing 1 papers\n", + "Error: The file './download/output/1903.03894v4.json' was not found.\n" ] } ], @@ -1679,14 +1416,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Successfully imported 8 sections from ./examples/csv_data/csv_arxiv_sections_example.csv\n" + "Error: CSV file ./examples/csv_data/csv_arxiv_sections_example.csv does not exist.\n" ] } ], @@ -1704,14 +1441,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Successfully imported 8 sections from ./examples/json_data/json_arxiv_sections_example.json\n" + "Error: JSON file ./examples/json_data/json_arxiv_sections_example.json does not exist.\n" ] } ], @@ -1730,20 +1467,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "id": "insert-sections", "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Inserted section: Introduction\n", - "Inserted section: Background\n", - "Inserted section: Model Architecture\n", - "Inserted section: Training\n", - "Inserted section: Results\n", - "Inserted section: Conclusion\n" + "ename": "TypeError", + "evalue": "SQLArxivSections.insert_section() got an unexpected keyword argument 'appendix'", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mTypeError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[39]\u001b[39m\u001b[32m, line 48\u001b[39m\n\u001b[32m 2\u001b[39m sections = [\n\u001b[32m 3\u001b[39m {\n\u001b[32m 4\u001b[39m \u001b[33m'\u001b[39m\u001b[33mcontent\u001b[39m\u001b[33m'\u001b[39m: \u001b[33m'\u001b[39m\u001b[33mThe dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder...\u001b[39m\u001b[33m'\u001b[39m,\n\u001b[32m (...)\u001b[39m\u001b[32m 44\u001b[39m }\n\u001b[32m 45\u001b[39m ]\n\u001b[32m 47\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m section \u001b[38;5;129;01min\u001b[39;00m sections:\n\u001b[32m---> \u001b[39m\u001b[32m48\u001b[39m \u001b[43mresearch_arcade\u001b[49m\u001b[43m.\u001b[49m\u001b[43minsert_node\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43marxiv_sections\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnode_features\u001b[49m\u001b[43m=\u001b[49m\u001b[43msection\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 49\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mInserted section: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00msection[\u001b[33m'\u001b[39m\u001b[33mtitle\u001b[39m\u001b[33m'\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n", + "\u001b[36mFile \u001b[39m\u001b[32m/data/cl195/paper-crawler/research_arcade/research_arcade.py:136\u001b[39m, in \u001b[36mResearchArcade.insert_node\u001b[39m\u001b[34m(self, table, node_features)\u001b[39m\n\u001b[32m 134\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.arxiv_paragraphs.insert_paragraph(**node_features)\n\u001b[32m 135\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m table == \u001b[33m'\u001b[39m\u001b[33marxiv_sections\u001b[39m\u001b[33m'\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m136\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43marxiv_sections\u001b[49m\u001b[43m.\u001b[49m\u001b[43minsert_section\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mnode_features\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 137\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 138\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mTable \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtable\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m not found.\u001b[39m\u001b[33m\"\u001b[39m)\n", + "\u001b[31mTypeError\u001b[39m: SQLArxivSections.insert_section() got an unexpected keyword argument 'appendix'" ] } ], @@ -1809,7 +1546,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, "id": "get-all-sections", "metadata": {}, "outputs": [ @@ -1817,162 +1554,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "Total sections: id content title \\\n", - "0 1 The dominant sequence transduction models are ... Introduction \n", - "1 2 The goal of reducing sequential computation al... Background \n", - "2 3 The Transformer follows this overall architect... Model Architecture \n", - "3 4 We trained on the standard WMT 2014 English-Ge... Training \n", - "4 5 In this work we presented the Transformer, the... Conclusion \n", - "5 6 We introduce a new language representation mod... Introduction \n", - "6 7 Unlike recent language representation models, ... Related Work \n", - "7 8 BERT uses a multi-layer bidirectional Transfor... Model Architecture \n", - "8 9 The dominant sequence transduction models are ... Introduction \n", - "9 10 The goal of reducing sequential computation al... Background \n", - "10 11 The Transformer follows this overall architect... Model Architecture \n", - "11 12 We trained on the standard WMT 2014 English-Ge... Training \n", - "12 13 In this work we presented the Transformer, the... Conclusion \n", - "13 14 We introduce a new language representation mod... Introduction \n", - "14 15 Unlike recent language representation models, ... Related Work \n", - "15 16 BERT uses a multi-layer bidirectional Transfor... Model Architecture \n", - "16 17 The dominant sequence transduction models are ... Introduction \n", - "17 18 Most competitive neural sequence transduction ... Background \n", - "18 19 Most neural sequence transduction models have ... Model Architecture \n", - "19 20 In this section we describe the training regim... Training \n", - "20 21 On the WMT 2014 English-to-German translation ... Results \n", - "21 22 In this work, we presented the Transformer, th... Conclusion \n", - "22 23 \\n\\label{sec:intro}\\nIn recent years there has... Introduction \n", - "23 24 \\n\\nOur work builds upon a rich line of recent... Related Work \n", - "24 25 \\n\\label{sec:proposed}\\n\\nThe key idea of \\nam... Proposed Method \n", - "25 26 \\n\\label{sec:ex}\\n\\nWe evaluate the benefits o... Experiments \n", - "26 27 \\n\\nWe introduced a differentiable pooling met... Conclusion \n", - "27 28 \\nThis research has been supported in part by ... Acknowledgement \n", - "28 29 The dominant sequence transduction models are ... Introduction \n", - "29 30 The goal of reducing sequential computation al... Background \n", - "30 31 The Transformer follows this overall architect... Model Architecture \n", - "31 32 We trained on the standard WMT 2014 English-Ge... Training \n", - "32 33 In this work we presented the Transformer, the... Conclusion \n", - "33 34 We introduce a new language representation mod... Introduction \n", - "34 35 Unlike recent language representation models, ... Related Work \n", - "35 36 BERT uses a multi-layer bidirectional Transfor... Model Architecture \n", - "36 37 The dominant sequence transduction models are ... Introduction \n", - "37 38 The goal of reducing sequential computation al... Background \n", - "38 39 The Transformer follows this overall architect... Model Architecture \n", - "39 40 We trained on the standard WMT 2014 English-Ge... Training \n", - "40 41 In this work we presented the Transformer, the... Conclusion \n", - "41 42 We introduce a new language representation mod... Introduction \n", - "42 43 Unlike recent language representation models, ... Related Work \n", - "43 44 BERT uses a multi-layer bidirectional Transfor... Model Architecture \n", - "44 45 The dominant sequence transduction models are ... Introduction \n", - "45 46 Most competitive neural sequence transduction ... Background \n", - "46 47 Most neural sequence transduction models have ... Model Architecture \n", - "47 48 In this section we describe the training regim... Training \n", - "48 49 On the WMT 2014 English-to-German translation ... Results \n", - "49 50 In this work, we presented the Transformer, th... Conclusion \n", - "\n", - " appendix paper_arxiv_id section_in_paper_id \n", - "0 False 1706.03762v7 1.0 \n", - "1 False 1706.03762v7 2.0 \n", - "2 False 1706.03762v7 3.0 \n", - "3 False 1706.03762v7 4.0 \n", - "4 False 1706.03762v7 5.0 \n", - "5 False 1810.04805v2 1.0 \n", - "6 False 1810.04805v2 2.0 \n", - "7 False 1810.04805v2 3.0 \n", - "8 False 1706.03762v7 1.0 \n", - "9 False 1706.03762v7 2.0 \n", - "10 False 1706.03762v7 3.0 \n", - "11 False 1706.03762v7 4.0 \n", - "12 False 1706.03762v7 5.0 \n", - "13 False 1810.04805v2 1.0 \n", - "14 False 1810.04805v2 2.0 \n", - "15 False 1810.04805v2 3.0 \n", - "16 False 1706.03762v7 1.0 \n", - "17 False 1706.03762v7 2.0 \n", - "18 False 1706.03762v7 3.0 \n", - "19 False 1706.03762v7 4.0 \n", - "20 False 1706.03762v7 5.0 \n", - "21 False 1706.03762v7 6.0 \n", - "22 False 1806.08804v4 1.0 \n", - "23 False 1806.08804v4 2.0 \n", - "24 False 1806.08804v4 3.0 \n", - "25 False 1806.08804v4 4.0 \n", - "26 False 1806.08804v4 5.0 \n", - "27 False 1806.08804v4 6.0 \n", - "28 False 1706.03762v7 1.0 \n", - "29 False 1706.03762v7 2.0 \n", - "30 False 1706.03762v7 3.0 \n", - "31 False 1706.03762v7 4.0 \n", - "32 False 1706.03762v7 5.0 \n", - "33 False 1810.04805v2 1.0 \n", - "34 False 1810.04805v2 2.0 \n", - "35 False 1810.04805v2 3.0 \n", - "36 False 1706.03762v7 1.0 \n", - "37 False 1706.03762v7 2.0 \n", - "38 False 1706.03762v7 3.0 \n", - "39 False 1706.03762v7 4.0 \n", - "40 False 1706.03762v7 5.0 \n", - "41 False 1810.04805v2 1.0 \n", - "42 False 1810.04805v2 2.0 \n", - "43 False 1810.04805v2 3.0 \n", - "44 False 1706.03762v7 1.0 \n", - "45 False 1706.03762v7 2.0 \n", - "46 False 1706.03762v7 3.0 \n", - "47 False 1706.03762v7 4.0 \n", - "48 False 1706.03762v7 5.0 \n", - "49 False 1706.03762v7 6.0 \n", + "Total sections: [(1, \"\\n\\\\label{sec:intro}\\nIn recent years there has been a surge of interest in developing graph neural networks (GNNs)---general deep learning architectures that can operate over graph structured data, such as social network data \\\\cite{hamilton2017inductive,kipf2017semi,Vel+2018} or graph-based representations of molecules \\\\cite{dai2016discriminative,Duv+2015,Gil+2017}.\\nThe general approach with GNNs is to view the underlying graph as a computation graph and learn neural network primitives that generate individual node embeddings by passing, transforming, and aggregating node feature information across the graph~\\\\cite{Gil+2017,hamilton2017inductive}.\\nThe generated node embeddings can then be used as input to any differentiable prediction layer, e.g., for node classification \\\\cite{hamilton2017inductive} or link prediction \\\\cite{Sch+2017}, and the whole model can be trained in an end-to-end fashion. \\n\\n\\n\\n\\nHowever, a major limitation of current GNN architectures is that they are inherently {\\\\em flat}\\\\em flat as they only propagate information across the edges of the graph and are unable to infer and aggregate the information in a \\\\textit{hierarchical} way. \\nFor example, in order to successfully encode the graph structure of organic molecules, one would ideally want to encode the local molecular structure (e.g., individual atoms and their direct bonds) as well as the coarse-grained structure of the molecular graph (e.g., groups of atoms and bonds representing functional units in a molecule).\\nThis lack of hierarchical structure is especially problematic for the task of graph classification, where the goal is to predict the label associated with an \\\\textit{entire graph}. When applying GNNs to graph classification, the standard approach is to generate embeddings for all the nodes in the graph and then to {\\\\em globally pool}\\\\em globally pool all these node embeddings together, e.g., using a simple summation or neural network that operates over sets \\\\cite{dai2016discriminative,Duv+2015,Gil+2017,Li+2016}. This global pooling approach ignores any hierarchical structure that might be present in the graph, and it prevents researchers from building effective GNN models for predictive tasks over entire graphs.\\n\\n\\n\\n\\n\\n\\n\\nHere we propose \\\\name, a differentiable graph pooling module that can be adapted to various graph neural network architectures in an hierarchical and end-to-end fashion (Figure~\\\\ref{fig:illustration}). \\n\\\\name allows for developing deeper GNN models that can learn to operate on hierarchical representations of a graph. We develop a graph analogue of the spatial pooling operation in CNNs \\\\cite{krizhevsky2012imagenet}, which allows for deep CNN architectures to iteratively operate on coarser and coarser representations of an image. The challenge in the GNN setting---compared to standard CNNs---is that graphs contain no natural notion of spatial locality, i.e., one cannot simply pool together all nodes in a ``$m \\\\times m$m \\\\times m patch'' on a graph, because the complex topological structure of graphs precludes any straightforward, deterministic definition of a ``patch''. Moreover, unlike image data, graph data sets often contain graphs with varying numbers of nodes and edges, which makes defining a general graph pooling operator even more challenging.\\n\\nIn order to solve the above challenges, we require a model that learns how to cluster together nodes to build a hierarchical multi-layer scaffold on top of the underlying graph. \\nOur approach \\\\name learns a differentiable soft assignment at each layer of a deep GNN, mapping nodes to a set of clusters based on their learned embeddings. \\nIn this framework, we generate deep GNNs by ``stacking'' GNN layers in a hierarchical fashion (Figure~\\\\ref{fig:illustration}): the input nodes at the layer $l$l GNN module correspond to the clusters learned at the layer $l-1$l-1 GNN module. \\nThus, each layer of \\\\name coarsens the input graph more and more, and \\\\name is able to generate a hierarchical representation of any input graph after training.\\nWe show that \\\\name\\\\ can be combined with various GNN approaches, resulting in an average 7\\\\% gain in accuracy and a new state of the art on four out of five benchmark graph classification tasks. \\nFinally, we show that \\\\name\\\\ can learn interpretable hierarchical clusters that correspond to well-defined communities in the input graphs.\\n\\\\cut{\\n\\\\jure{Say much much more about our method, how it works and why it is cool.}\\n\\n\\\\chris{Maybe add highlevel visualization of the idea.}\\n\\n\\\\jure{ I strongly agree, we need a Figure 1 with an illustration of the method.\\nFor example, a graph and then a hierarchical multi-layer scaffold on top of it.\\nWill, you are great at creating good figures. Do you want to give it a try?}\\n\\\\will{Yes, I will brainstorm with Rex today about a figure and put something together :)}\\n\\n\\\\chris{Quickly sketched this. I think it somehow explains the high-level idea. Guess the equations are too much.}\\n}\\n\\\\jure{Say much much more about our method, how it works and why it is cool.}Say much much more about our method, how it works and why it is cool.\\n\\n\\\\chris{Maybe add highlevel visualization of the idea.}Maybe add highlevel visualization of the idea.\\n\\n\\\\jure{ I strongly agree, we need a Figure 1 with an illustration of the method.\\nFor example, a graph and then a hierarchical multi-layer scaffold on top of it.\\nWill, you are great at creating good figures. Do you want to give it a try?} I strongly agree, we need a Figure 1 with an illustration of the method.\\nFor example, a graph and then a hierarchical multi-layer scaffold on top of it.\\nWill, you are great at creating good figures. Do you want to give it a try?\\n\\\\will{Yes, I will brainstorm with Rex today about a figure and put something together :)}Yes, I will brainstorm with Rex today about a figure and put something together :)\\n\\n\\\\chris{Quickly sketched this. I think it somehow explains the high-level idea. Guess the equations are too much.}Quickly sketched this. I think it somehow explains the high-level idea. Guess the equations are too much.\\n\\n\\n\\\\begin{figure}[t]\\\\begin{center}\\n \\\\includegraphics[scale=0.95]{figs/differentiable-pooling-V2.pdf}\\n \\\\caption{High-level illustration of our proposed method \\\\name. At each hierarchical layer, we run a GNN model to obtain embeddings of nodes. We then use these learned embeddings to cluster nodes together and run another GNN layer on this coarsened graph. This whole process is repeated for $L$ layers and we use the final output representation to classify the graph. }\\n \\\\label{fig:illustration}\\n \\\\end{center}\\n\\\\end{figure}\\n \\\\includegraphics[scale=0.95]{figs/differentiable-pooling-V2.pdf}\\n \\\\caption{High-level illustration of our proposed method \\\\name. At each hierarchical layer, we run a GNN model to obtain embeddings of nodes. We then use these learned embeddings to cluster nodes together and run another GNN layer on this coarsened graph. This whole process is repeated for $L$ layers and we use the final output representation to classify the graph. }\\n \\\\label{fig:illustration}\\n \\n\\n\\n\\n\", 'Introduction', False, '1806.08804v4'), (2, \"\\n\\nOur work builds upon a rich line of recent research on graph neural networks and graph classification. \\n\\n\\\\xhdr{General graph neural networks}General graph neural networks\\nA wide variety of graph neural network (GNN) models have been proposed in recent years, including methods inspired by convolutional neural networks \\\\cite{Bru+2014,Def+2015,Duv+2015,hamilton2017inductive,kipf2017semi,Lei+2017,niepert2016learning, Vel+2018}, recurrent neural networks \\\\cite{Li+2016}, recursive neural networks~\\\\cite{bianchini2001,Sca+2009} and loopy belief propagation \\\\cite{dai2016discriminative}. \\nMost of these approaches fit within the framework of ``neural message passing'' proposed by Gilmer \\\\etal~\\\\cite{Gil+2017}. \\nIn the message passing framework, a GNN is viewed as a message passing algorithm where node representations are iteratively computed from the features of their neighbor nodes using a differentiable aggregation function.\\nHamilton \\\\etal~\\\\cite{Ham+2017a} provides a conceptual review of recent advancements in this area, and Bronstein \\\\etal \\\\cite{bronstein2017geometric} outlines connections to spectral graph convolutions. \\n\\n\\\\xhdr{Graph classification with graph neural networks}Graph classification with graph neural networks\\nGNNs have been applied to a wide variety of tasks, including node classification \\\\cite{hamilton2017inductive,kipf2017semi}, link prediction \\\\cite{kipf2018}, graph classification \\\\cite{dai2016discriminative,Duv+2015,zhang2018end}, and chemoinformatics~\\\\cite{Mer+2005,Lus+2013,Fou+2017,Jin+2018,Sch+2017}. \\nIn the context of graph classification---the task that we study here---a major challenge in applying GNNs is going from node embeddings, which are the output of GNNs, to a representation of the entire graph. \\nCommon approaches to this problem include simply summing up or averaging all the node embeddings in a final layer \\\\cite{Duv+2015}, introducing a ``virtual node'' that is connected to all the nodes in the graph \\\\cite{Li+2016}, or aggregating the node embeddings using a deep learning architecture that operates over sets \\\\cite{Gil+2017}.\\nHowever, all of these methods have the limitation that they do not learn hierarchical representations (i.e., all the node embeddings are globally pooled together in a single layer), and thus are unable to capture the natural structures of many real-world graphs.\\nSome recent approaches have also proposed applying CNN architectures to the concatenation of all the node embeddings \\\\cite{niepert2016learning,zhang2018end}, but this requires a specifying (or learning) a canonical ordering over nodes, which is in general very difficult and equivalent to solving graph isomorphism. \\n\\nLastly, there are some recent works that learn hierarchical graph representations by combining GNNs with deterministic graph clustering algorithms \\\\cite{Def+2015,simonovsky2017dynamic,Fey+2018}, following a two-stage approach. However, unlike these previous approaches, we seek to {\\\\em learn}\\\\em learn the hierarchical structure in an end-to-end fashion, rather than relying on a deterministic graph clustering subroutine.\\n\\n\\n\\n\\\\cut{\\n% Will: Let's try to move some of this to related work\\n\\\\rex{need to be removed probably. there are some points i think are important which is not illustrated in related work}\\nTo achieve this task. recent works have made several attempts.\\nRecent attempts include the use of linearization of graphs. A graph can be linearized to a vector representation using ``canonical ordering'' \\\\cite{niepert2016learning}. Pooling or striding is easy to carry out on the 1D representation. However, it is non-trivial to specify a canonical ordering that also preserves graph structure. For example, nodes that are distant in graph might be adjacent to each other in the linearized representation.\\n\\nAnother intuitive pooling strategy is to pool using a graph clustering or coarsening strategy \\\\cite{safro2015advanced}, which can be applied hierarchically and produce coarser and coarser graphs.\\nHowever, a variety of clustering and coarsening strategies have been designed, and significant hand-engineering is required to find the best strategy for a specific classification task at hand.\\n}\\n\\\\rex{need to be removed probably. there are some points i think are important which is not illustrated in related work}need to be removed probably. there are some points i think are important which is not illustrated in related work\\nTo achieve this task. recent works have made several attempts.\\nRecent attempts include the use of linearization of graphs. A graph can be linearized to a vector representation using ``canonical ordering'' \\\\cite{niepert2016learning}. Pooling or striding is easy to carry out on the 1D representation. However, it is non-trivial to specify a canonical ordering that also preserves graph structure. For example, nodes that are distant in graph might be adjacent to each other in the linearized representation.\\n\\nAnother intuitive pooling strategy is to pool using a graph clustering or coarsening strategy \\\\cite{safro2015advanced}, which can be applied hierarchically and produce coarser and coarser graphs.\\nHowever, a variety of clustering and coarsening strategies have been designed, and significant hand-engineering is required to find the best strategy for a specific classification task at hand.\\n\\n\\n\\n\\n\\\\cut{\\n\\\\xhdr{Graph kernels}\\n\\nIn recent years, various graph kernels have been proposed, which\\nimplicitly or explicitly map graphs to a Hilbert space. Gärtner \\\\etal\\\\cite{Gae+2003}\\nand Kashima \\\\etal~\\\\cite{Kas+2003} simultaneously proposed\\ngraph kernels based on random walks, which count the number of walks\\ntwo graphs have in common. Since then, random walk kernels have been\\nstudied intensively, e.g., see~\\\\cite{Sug+2015}.\\nKernels based on shortest paths were first proposed in~\\\\cite{Borgwardt2005}.\\n\\nA different line in the development of graph kernels focused\\nparticularly on scalable graph kernels. These kernels are typically\\ncomputed efficiently by explicit feature maps, which allow to bypass\\nthe computation of a gram matrix, and allow applying scalable linear\\nclassification algorithms. Prominent examples are kernels based on\\nsubgraphs up to a fixed size, so called\\n{graphlets}~\\\\cite{She+2009}. Other approaches of this category encode\\nthe neighborhood of every node by different techniques, e.g.,\\nsee~\\\\cite{Hid+2009, Neu+2016}, and most notably the\\nWeisfeiler-Lehman subtree kernel~\\\\cite{She+2011} and its higher-order\\nvariants~\\\\cite{Mor+2017}. Subgraph and Weisfeiler-Lehman kernels have\\nbeen successfully employed within frameworks for smoothed and deep\\ngraph kernels~\\\\cite{Yan+2015,Yan+2015a}. Recent developments include\\nassignment-based approaches~\\\\cite{kriege2016valid,Nik+2017,Joh+2015} and\\nspectral approaches~\\\\cite{Kon+2016}. Although graph kernels have shown good performance in the past, they lack the ability to adapt to they give data distribution at hand, since they mostly rely on precomputed features. Moreover, they may not scale to large data sets due to the quadratic overhead to compute the gram matrix. \\n\\nNiepert \\\\etal~\\\\cite{niepert2016learning} extracted canonical vector representations of neighborhoods of nodes, based on heuristics such as the Weisfeiler-Lehman algorithm~\\\\cite{She+2011}, to compute a graph embeddings and then used neural networks for the classification task. Moreover, recently graph neural networks (GNN) for node and graph classification became popular. Most of these\\napproaches fit into the framework proposed by~\\\\cite{Gil+2017}. Here a GNN is viewed as a message passing approach where node\\nfeatures are iteratively computed from the features of the\\nnode's neighbors by using a differentialable neighborhood aggregation function. The parameters of this function are \\nlearned together with the parameters of the neural network used for the\\nclassification task in an end-to-end fashion. Up to now, naïve global mean pooling or precomputed clustering methods were applied to compute graph embeddings from the features of each single node.\\n\\nNotable instances of this model include Neural\\nFingerprints~\\\\cite{Duv+2015}, Gated Graph Neural\\nNetworks~\\\\cite{Li+2016}, and spectral approaches proposed\\nin~\\\\cite{Bru+2014,Def+2015,kipf2017semi}. Dai \\\\etal\\\\cite{dai2016discriminative}\\nproposed an approach inspired by mean-field inference\\nand Lei \\\\etal\\\\cite{Lei+2017} showed that the generated\\nfeature maps lie in the same Hilbert space as some popular graph\\nkernels and successfully applied them in the domain of\\nchemoinformatics~\\\\cite{Jin+2018}. In~\\\\cite{simonovsky2017dynamic} GNN were extended to include edge features and various precomputed pooling heuristics based on clustering methods were applied. Attention-based extensions were explored in~\\\\cite{Vel+2018}. In order to make GNNs scale to large graphs Hamilton \\\\etal\\\\cite{hamilton2017inductive} and Chen \\\\etal\\\\cite{Che+2018} devised stochastic versions of GNNs. Recently, a differentiable pooling mechanism to compute graph embeddings based on node features using differentiable sorting was proposed~\\\\cite{zhang2018end}.\\n\\nMoreover, GNNs were applied for protein-protein\\ninteraction prediction~\\\\cite{Fou+2017} and quantum interactions in\\nmolecules~\\\\cite{Sch+2017}. An approach for unsupervised learning based\\non GNNs was presented in~\\\\cite{Gar+2017}. Early\\napproaches were published in~\\\\cite{Mer+2005} and~\\\\cite{Sca+2009,\\nSca+2009a}. A survey can be found in~\\\\cite{Ham+2017a}.\\n\\n}\\n\\\\xhdr{Graph kernels}Graph kernels\\n\\nIn recent years, various graph kernels have been proposed, which\\nimplicitly or explicitly map graphs to a Hilbert space. Gärtner \\\\etal\\\\cite{Gae+2003}\\nand Kashima \\\\etal~\\\\cite{Kas+2003} simultaneously proposed\\ngraph kernels based on random walks, which count the number of walks\\ntwo graphs have in common. Since then, random walk kernels have been\\nstudied intensively, e.g., see~\\\\cite{Sug+2015}.\\nKernels based on shortest paths were first proposed in~\\\\cite{Borgwardt2005}.\\n\\nA different line in the development of graph kernels focused\\nparticularly on scalable graph kernels. These kernels are typically\\ncomputed efficiently by explicit feature maps, which allow to bypass\\nthe computation of a gram matrix, and allow applying scalable linear\\nclassification algorithms. Prominent examples are kernels based on\\nsubgraphs up to a fixed size, so called\\n{graphlets}graphlets~\\\\cite{She+2009}. Other approaches of this category encode\\nthe neighborhood of every node by different techniques, e.g.,\\nsee~\\\\cite{Hid+2009, Neu+2016}, and most notably the\\nWeisfeiler-Lehman subtree kernel~\\\\cite{She+2011} and its higher-order\\nvariants~\\\\cite{Mor+2017}. Subgraph and Weisfeiler-Lehman kernels have\\nbeen successfully employed within frameworks for smoothed and deep\\ngraph kernels~\\\\cite{Yan+2015,Yan+2015a}. Recent developments include\\nassignment-based approaches~\\\\cite{kriege2016valid,Nik+2017,Joh+2015} and\\nspectral approaches~\\\\cite{Kon+2016}. Although graph kernels have shown good performance in the past, they lack the ability to adapt to they give data distribution at hand, since they mostly rely on precomputed features. Moreover, they may not scale to large data sets due to the quadratic overhead to compute the gram matrix. \\n\\nNiepert \\\\etal~\\\\cite{niepert2016learning} extracted canonical vector representations of neighborhoods of nodes, based on heuristics such as the Weisfeiler-Lehman algorithm~\\\\cite{She+2011}, to compute a graph embeddings and then used neural networks for the classification task. Moreover, recently graph neural networks (GNN) for node and graph classification became popular. Most of these\\napproaches fit into the framework proposed by~\\\\cite{Gil+2017}. Here a GNN is viewed as a message passing approach where node\\nfeatures are iteratively computed from the features of the\\nnode's neighbors by using a differentialable neighborhood aggregation function. The parameters of this function are \\nlearned together with the parameters of the neural network used for the\\nclassification task in an end-to-end fashion. Up to now, naïve global mean pooling or precomputed clustering methods were applied to compute graph embeddings from the features of each single node.\\n\\nNotable instances of this model include Neural\\nFingerprints~\\\\cite{Duv+2015}, Gated Graph Neural\\nNetworks~\\\\cite{Li+2016}, and spectral approaches proposed\\nin~\\\\cite{Bru+2014,Def+2015,kipf2017semi}. Dai \\\\etal\\\\cite{dai2016discriminative}\\nproposed an approach inspired by mean-field inference\\nand Lei \\\\etal\\\\cite{Lei+2017} showed that the generated\\nfeature maps lie in the same Hilbert space as some popular graph\\nkernels and successfully applied them in the domain of\\nchemoinformatics~\\\\cite{Jin+2018}. In~\\\\cite{simonovsky2017dynamic} GNN were extended to include edge features and various precomputed pooling heuristics based on clustering methods were applied. Attention-based extensions were explored in~\\\\cite{Vel+2018}. In order to make GNNs scale to large graphs Hamilton \\\\etal\\\\cite{hamilton2017inductive} and Chen \\\\etal\\\\cite{Che+2018} devised stochastic versions of GNNs. Recently, a differentiable pooling mechanism to compute graph embeddings based on node features using differentiable sorting was proposed~\\\\cite{zhang2018end}.\\n\\nMoreover, GNNs were applied for protein-protein\\ninteraction prediction~\\\\cite{Fou+2017} and quantum interactions in\\nmolecules~\\\\cite{Sch+2017}. An approach for unsupervised learning based\\non GNNs was presented in~\\\\cite{Gar+2017}. Early\\napproaches were published in~\\\\cite{Mer+2005} and~\\\\cite{Sca+2009,\\nSca+2009a}. A survey can be found in~\\\\cite{Ham+2017a}.\\n\\n\\n\\n\\n\\n\\n\\n\", 'Related Work', False, '1806.08804v4'), (3, \"\\n\\\\label{sec:proposed}\\n\\nThe key idea of \\\\name is that it enables the construction of deep, multi-layer GNN models by providing a differentiable module to hierarchically pool graph nodes. \\nIn this section, we outline the \\\\name module and show how it is applied in a deep GNN architecture.\\n\\n\\n\\n\\\\subsection{Preliminaries}\\n\\\\label{sec:setting}\\n\\nWe represent a graph $G$G as $(A,F)$(A,F), where $A\\\\in \\\\{0, 1\\\\}^{n\\\\times n}$A\\\\in \\\\{0, 1\\\\}^{n\\\\times n}n\\\\times n is the adjacency matrix, and $F \\\\in \\\\mathbb{R}^{n\\\\times d}$F \\\\in \\\\mathbb{R}^{n\\\\times d}n\\\\times d is the node feature matrix assuming each node has $d$d features.\\\\footnote{We do not consider edge features, although one can easily extend the algorithm to support edge features using techniques introduced in \\\\cite{simonovsky2017dynamic}.}\\nGiven a set of labeled graphs $\\\\mathcal{D}=\\\\{(G_1,y_1),(G_2,y_2),...\\\\}$\\\\mathcal{D}=\\\\{(G_1,y_1),(G_2,y_2),...\\\\} where $y_i \\\\in \\\\mathcal{Y}$y_i \\\\in \\\\mathcal{Y} is the label corresponding to graph $G_i \\\\in \\\\mathcal{G}$G_i \\\\in \\\\mathcal{G}, the goal of graph classification is to learn a mapping $f : \\\\mathcal{G} \\\\to \\\\mathcal{Y}$f : \\\\mathcal{G} \\\\to \\\\mathcal{Y} that maps graphs to the set of labels. \\nThe challenge---compared to standard supervised machine learning setup---is that we need a way to extract useful feature vectors from these input graphs.\\nThat is, in order to apply standard machine learning methods for classification, e.g., neural networks, we need a procedure to convert each graph to an finite dimensional vector in $\\\\mathbb{R}^D$\\\\mathbb{R}^D.\\n\\n\\n\\n\\\\xhdr{Graph neural networks}Graph neural networks\\nIn this work, we build upon graph neural networks in order to learn useful representations for graph classification in an end-to-end fashion. \\nIn particular, we consider GNNs that employ the following general ``message-passing'' architecture:\\n\\\\begin{equation}\\\\label{eq:gnn}\\n H^{(k)} = M(A,H^{(k-1)};\\\\theta^{(k)}),\\n\\\\end{equation}\\\\begin{equation}\\\\label{eq:gnn}\\n H^{(k)} = M(A,H^{(k-1)};\\\\theta^{(k)}),\\n\\\\end{equation}\\\\label{eq:gnn}\\n H^{(k)}(k) = M(A,H^{(k-1)}(k-1);\\\\theta^{(k)}(k)),\\n\\nwhere $H^{(k)} \\\\in \\\\mathbb{R}^{n \\\\times d}$H^{(k)}(k) \\\\in \\\\mathbb{R}^{n \\\\times d}n \\\\times d are the node embeddings (i.e., ``messages'') computed after $k$k steps of the GNN and $M$M is the message propagation function, which depends on the adjacency matrix, trainable parameters $\\\\theta^{(k)}$\\\\theta^{(k)}(k), and the node embeddings $H^{(k-1)}$H^{(k-1)}(k-1) generated from the previous message-passing step.\\\\footnote{For notational convenience, we assume that the embedding dimension is $d$ for all $H^{(k)}$; however, in general this restriction is not necessary.}\\nThe input node embeddings $H^{(0)}$H^{(0)}(0) at the initial message-passing iteration $(k=1)$(k=1), are initialized using\\nthe node features on the graph, $H^{(0)} = F$H^{(0)}(0) = F. \\n\\nThere are many possible implementations of the propagation function $M$M \\\\cite{Gil+2017,hamilton2017inductive}.\\nFor example, one popular variant of GNNs---Kipf's \\\\etal \\\\cite{kipf2017semi} Graph Convolutional Networks (GCNs)---implements $M$M using a combination of linear transformations and ReLU non-linearities:\\n\\\\begin{equation}\\n \\\\label{eq:gcn}\\n H^{(k)} = M(A,H^{(k-1)};W^{(k)}) = \\\\textrm{ReLU}(\\\\tilde{D}^{-\\\\frac{1}{2}}\\\\tilde{A}\\\\tilde{D}^{-\\\\frac{1}{2}} H^{(k-1)} W^{(k-1)}),\\n\\\\end{equation}\\\\begin{equation}\\n \\\\label{eq:gcn}\\n H^{(k)} = M(A,H^{(k-1)};W^{(k)}) = \\\\textrm{ReLU}(\\\\tilde{D}^{-\\\\frac{1}{2}}\\\\tilde{A}\\\\tilde{D}^{-\\\\frac{1}{2}} H^{(k-1)} W^{(k-1)}),\\n\\\\end{equation}\\n \\\\label{eq:gcn}\\n H^{(k)}(k) = M(A,H^{(k-1)}(k-1);W^{(k)}(k)) = \\\\textrm{ReLU}(\\\\tilde{D}^{-\\\\frac{1}{2}}-\\\\frac{1}{2}\\\\tilde{A}\\\\tilde{D}^{-\\\\frac{1}{2}}-\\\\frac{1}{2} H^{(k-1)}(k-1) W^{(k-1)}(k-1)),\\n\\nwhere $\\\\tilde{A}=A+I$\\\\tilde{A}=A+I, $\\\\tilde{D}=\\\\sum_j\\\\tilde{A}_{ij}$\\\\tilde{D}=\\\\sum_j\\\\tilde{A}_{ij}ij and $W^{(k)} \\\\in \\\\mathbb{R}^{d \\\\times d}$W^{(k)}(k) \\\\in \\\\mathbb{R}^{d \\\\times d}d \\\\times d is a trainable weight matrix.\\nThe differentiable pooling model we propose can be applied to any GNN model implementing Equation \\\\eqref{eq:gnn}, and is agnostic with regards to the specifics of how $M$M is implemented. \\n\\nA full GNN module will run $K$K iterations of Equation \\\\eqref{eq:gnn} to generate the final output node embeddings $Z=H^{(K)} \\\\in \\\\mathbb{R}^{n \\\\times d}$Z=H^{(K)}(K) \\\\in \\\\mathbb{R}^{n \\\\times d}n \\\\times d, where $K$K is usually in the range 2-6.\\nFor simplicity, in the following sections we will abstract away the internal structure of the GNNs and use $Z = \\\\gnn(A, X)$Z = \\\\gnn(A, X) to denote an arbitrary GNN module implementing $K$K iterations of message passing according to some adjacency matrix $A$A and initial input node features $X$X. \\n\\n\\\\xhdr{Stacking GNNs and pooling layers}Stacking GNNs and pooling layers\\nGNNs implementing Equation \\\\eqref{eq:gnn} are inherently flat, as they only propagate information across edges of a graph.\\nThe goal of this work is to define a general, end-to-end differentiable strategy that allows one to {\\\\em stack}\\\\em stack multiple GNN modules in a hierarchical fashion. \\nFormally, given $Z = \\\\gnn(A, X)$Z = \\\\gnn(A, X), the output of a GNN module, and a graph adjacency matrix $A \\\\in \\\\mathbb{R}^{n \\\\times n}$A \\\\in \\\\mathbb{R}^{n \\\\times n}n \\\\times n, we seek to define a strategy to output a new coarsened graph containing $m \u001b[39m\u001b[32m2\u001b[39m \u001b[43mresearch_arcade\u001b[49m\u001b[43m.\u001b[49m\u001b[43mconstruct_table_from_api\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43marxiv_paragraphs\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m/data/cl195/paper-crawler/research_arcade/research_arcade.py:585\u001b[39m, in \u001b[36mResearchArcade.construct_table_from_api\u001b[39m\u001b[34m(self, table, config)\u001b[39m\n\u001b[32m 583\u001b[39m \u001b[38;5;28mself\u001b[39m.arxiv_sections.construct_sections_table_from_api(**config)\n\u001b[32m 584\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m table == \u001b[33m\"\u001b[39m\u001b[33marxiv_paragraphs\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m585\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43marxiv_paragraphs\u001b[49m\u001b[43m.\u001b[49m\u001b[43mconstruct_paragraphs_table_from_api\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 586\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m table == \u001b[33m\"\u001b[39m\u001b[33marxiv_categories\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m 587\u001b[39m \u001b[38;5;28mself\u001b[39m.arxiv_categories.construct_category_table_from_api(**config)\n", + "\u001b[36mFile \u001b[39m\u001b[32m/data/cl195/paper-crawler/research_arcade/sql_database/sql_arxiv_paragraphs.py:501\u001b[39m, in \u001b[36mSQLArxivParagraphs.construct_paragraphs_table_from_api\u001b[39m\u001b[34m(self, arxiv_ids, dest_dir)\u001b[39m\n\u001b[32m 499\u001b[39m id_number = get_paragraph_num(paragraph_id)\n\u001b[32m 500\u001b[39m id_zero_based = id_number - section_min_paragraph[(paper_arxiv_id, paper_section)]\n\u001b[32m--> \u001b[39m\u001b[32m501\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43minsert_paragraph\u001b[49m\u001b[43m(\u001b[49m\u001b[43mparagraph_id\u001b[49m\u001b[43m=\u001b[49m\u001b[43mid_zero_based\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcontent\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcontent\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpaper_arxiv_id\u001b[49m\u001b[43m=\u001b[49m\u001b[43mpaper_arxiv_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpaper_section\u001b[49m\u001b[43m=\u001b[49m\u001b[43mpaper_section\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m/data/cl195/paper-crawler/research_arcade/sql_database/sql_arxiv_paragraphs.py:77\u001b[39m, in \u001b[36mSQLArxivParagraphs.insert_paragraph\u001b[39m\u001b[34m(self, paragraph_id, content, paper_arxiv_id, paper_section, section_id, paragraph_in_paper_id)\u001b[39m\n\u001b[32m 75\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m 76\u001b[39m cur = conn.cursor()\n\u001b[32m---> \u001b[39m\u001b[32m77\u001b[39m \u001b[43mcur\u001b[49m\u001b[43m.\u001b[49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 78\u001b[39m \u001b[38;5;250;43m \u001b[39;49m\u001b[33;43;03m\"\"\"\u001b[39;49;00m\n\u001b[32m 79\u001b[39m \u001b[33;43;03m INSERT INTO arxiv_paragraphs (paragraph_id, content, paper_arxiv_id, paper_section)\u001b[39;49;00m\n\u001b[32m 80\u001b[39m \u001b[33;43;03m VALUES (%s, %s, %s, %s)\u001b[39;49;00m\n\u001b[32m 81\u001b[39m \u001b[33;43;03m ON CONFLICT ON CONSTRAINT ux_arxiv_paragraphs_unique DO NOTHING\u001b[39;49;00m\n\u001b[32m 82\u001b[39m \u001b[33;43;03m RETURNING id\u001b[39;49;00m\n\u001b[32m 83\u001b[39m \u001b[33;43;03m \"\"\"\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m 84\u001b[39m \u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mparagraph_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcontent\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpaper_arxiv_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpaper_section\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 85\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 86\u001b[39m res = cur.fetchone()\n\u001b[32m 87\u001b[39m cur.close()\n", + "\u001b[31mUndefinedObject\u001b[39m: constraint \"ux_arxiv_paragraphs_unique\" for table \"arxiv_paragraphs\" does not exist\n" + ] } ], "source": [ @@ -2139,14 +1706,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "No new paragraphs to import (all paragraphs already exist)\n" + "Error: CSV file ./examples/csv_data/csv_arxiv_paragraphs_example.csv does not exist.\n" ] } ], @@ -2190,19 +1757,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 44, "id": "insert-paragraphs", "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Inserted paragraph 1 from Introduction\n", - "Inserted paragraph 2 from Introduction\n", - "Inserted paragraph 3 from Introduction\n", - "Inserted paragraph 4 from Introduction\n", - "Inserted paragraph 5 from Introduction\n" + "ename": "UndefinedObject", + "evalue": "constraint \"ux_arxiv_paragraphs_unique\" for table \"arxiv_paragraphs\" does not exist\n", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mUndefinedObject\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[44]\u001b[39m\u001b[32m, line 46\u001b[39m\n\u001b[32m 2\u001b[39m paragraphs = [\n\u001b[32m 3\u001b[39m {\n\u001b[32m 4\u001b[39m \u001b[33m'\u001b[39m\u001b[33mparagraph_id\u001b[39m\u001b[33m'\u001b[39m: \u001b[32m1\u001b[39m,\n\u001b[32m (...)\u001b[39m\u001b[32m 42\u001b[39m }\n\u001b[32m 43\u001b[39m ]\n\u001b[32m 45\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m paragraph \u001b[38;5;129;01min\u001b[39;00m paragraphs:\n\u001b[32m---> \u001b[39m\u001b[32m46\u001b[39m \u001b[43mresearch_arcade\u001b[49m\u001b[43m.\u001b[49m\u001b[43minsert_node\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43marxiv_paragraphs\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnode_features\u001b[49m\u001b[43m=\u001b[49m\u001b[43mparagraph\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 47\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mInserted paragraph \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mparagraph[\u001b[33m'\u001b[39m\u001b[33mparagraph_id\u001b[39m\u001b[33m'\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m from \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mparagraph[\u001b[33m'\u001b[39m\u001b[33mpaper_section\u001b[39m\u001b[33m'\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n", + "\u001b[36mFile \u001b[39m\u001b[32m/data/cl195/paper-crawler/research_arcade/research_arcade.py:134\u001b[39m, in \u001b[36mResearchArcade.insert_node\u001b[39m\u001b[34m(self, table, node_features)\u001b[39m\n\u001b[32m 132\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.arxiv_papers.insert_paper(**node_features)\n\u001b[32m 133\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m table == \u001b[33m'\u001b[39m\u001b[33marxiv_paragraphs\u001b[39m\u001b[33m'\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m134\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43marxiv_paragraphs\u001b[49m\u001b[43m.\u001b[49m\u001b[43minsert_paragraph\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mnode_features\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 135\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m table == \u001b[33m'\u001b[39m\u001b[33marxiv_sections\u001b[39m\u001b[33m'\u001b[39m:\n\u001b[32m 136\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.arxiv_sections.insert_section(**node_features)\n", + "\u001b[36mFile \u001b[39m\u001b[32m/data/cl195/paper-crawler/research_arcade/sql_database/sql_arxiv_paragraphs.py:77\u001b[39m, in \u001b[36mSQLArxivParagraphs.insert_paragraph\u001b[39m\u001b[34m(self, paragraph_id, content, paper_arxiv_id, paper_section, section_id, paragraph_in_paper_id)\u001b[39m\n\u001b[32m 75\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m 76\u001b[39m cur = conn.cursor()\n\u001b[32m---> \u001b[39m\u001b[32m77\u001b[39m \u001b[43mcur\u001b[49m\u001b[43m.\u001b[49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 78\u001b[39m \u001b[38;5;250;43m \u001b[39;49m\u001b[33;43;03m\"\"\"\u001b[39;49;00m\n\u001b[32m 79\u001b[39m \u001b[33;43;03m INSERT INTO arxiv_paragraphs (paragraph_id, content, paper_arxiv_id, paper_section)\u001b[39;49;00m\n\u001b[32m 80\u001b[39m \u001b[33;43;03m VALUES (%s, %s, %s, %s)\u001b[39;49;00m\n\u001b[32m 81\u001b[39m \u001b[33;43;03m ON CONFLICT ON CONSTRAINT ux_arxiv_paragraphs_unique DO NOTHING\u001b[39;49;00m\n\u001b[32m 82\u001b[39m \u001b[33;43;03m RETURNING id\u001b[39;49;00m\n\u001b[32m 83\u001b[39m \u001b[33;43;03m \"\"\"\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m 84\u001b[39m \u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mparagraph_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcontent\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpaper_arxiv_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpaper_section\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 85\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 86\u001b[39m res = cur.fetchone()\n\u001b[32m 87\u001b[39m cur.close()\n", + "\u001b[31mUndefinedObject\u001b[39m: constraint \"ux_arxiv_paragraphs_unique\" for table \"arxiv_paragraphs\" does not exist\n" ] } ], @@ -2266,26 +1835,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 45, "id": "get-all-paragraphs", "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total paragraphs: 123\n", - "\n", - "First 3 paragraphs:\n", - " paragraph_id paper_section \\\n", - "0 0 Introduction \n", - "1 1 Introduction \n", - "2 2 Introduction \n", - "\n", - " content \n", - "0 \\label{sec:intro}\\nIn recent years there has b... \n", - "1 However, a major limitation of current GNN arc... \n", - "2 Here we propose \\name, a differentiable graph ... \n" + "ename": "TypeError", + "evalue": "object of type 'NoneType' has no len()", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mTypeError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[45]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m 1\u001b[39m paragraphs_df = research_arcade.get_all_node_features(\u001b[33m\"\u001b[39m\u001b[33marxiv_paragraphs\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mTotal paragraphs: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mparagraphs_df\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n\u001b[32m 3\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[33mFirst 3 paragraphs:\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 4\u001b[39m \u001b[38;5;28mprint\u001b[39m(paragraphs_df[[\u001b[33m'\u001b[39m\u001b[33mparagraph_id\u001b[39m\u001b[33m'\u001b[39m, \u001b[33m'\u001b[39m\u001b[33mpaper_section\u001b[39m\u001b[33m'\u001b[39m, \u001b[33m'\u001b[39m\u001b[33mcontent\u001b[39m\u001b[33m'\u001b[39m]].head(\u001b[32m3\u001b[39m))\n", + "\u001b[31mTypeError\u001b[39m: object of type 'NoneType' has no len()" ] } ], @@ -2321,7 +1883,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 48, "metadata": {}, "outputs": [ { @@ -2338,7 +1900,7 @@ " 'cited_arxiv_id': '1706.03762v7',\n", " 'bib_title': 'attention is all you need',\n", " 'bib_key': 'something',\n", - " 'citing_sections': 'citing_sections',\n", + " 'citing_sections': ['something'],\n", "}\n", "research_arcade.insert_edge(\"arxiv_citation\", edge_features=citation)\n", "print(\"Citation created!\")" @@ -2353,14 +1915,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 49, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "No new citations to import (all citations already exist)\n" + "Error: CSV file ./examples/csv_data/csv_arxiv_paper_citation_example.csv does not exist.\n" ] } ], @@ -2378,14 +1940,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 50, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "No new citations to import (all citations already exist)\n" + "Error: JSON file ./examples/json_data/json_arxiv_paper_citation_example.json does not exist.\n" ] } ], @@ -2403,41 +1965,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 52, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Total citations: 8\n", - " id citing_arxiv_id cited_arxiv_id \\\n", - "0 2 1706.03762v7 1409.0473v7 \n", - "1 3 1706.03762v7 1508.04025v5 \n", - "2 5 1810.04805v2 1802.05365v2 \n", - "3 6 2010.11929v2 1706.03762v7 \n", - "4 7 2010.11929v2 1810.04805v2 \n", - "\n", - " bib_title bib_key \\\n", - "0 Neural Machine Translation by Jointly Learning... bahdanau2014neural \n", - "1 Effective Approaches to Attention-based Neural... luong2015effective \n", - "2 Deep contextualized word representations peters2018deep \n", - "3 Attention Is All You Need vaswani2017attention \n", - "4 BERT: Pre-training of Deep Bidirectional Trans... devlin2018bert \n", - "\n", - " citing_sections citing_paragraphs \n", - "0 [\"introduction\", \"related_work\"] [] \n", - "1 [\"related_work\"] [] \n", - "2 [\"related_work\"] [] \n", - "3 [\"introduction\", \"model\"] [] \n", - "4 [\"related_work\"] [] \n" + "Total citations: 1\n", + "[(1, '1810.04805v2', '1706.03762v7', 'attention is all you need', 'something', None, ['something'], [])]\n" ] } ], "source": [ "all_citations = research_arcade.get_all_edge_features(\"arxiv_citation\")\n", "print(f\"Total citations: {len(all_citations)}\")\n", - "print(all_citations.head())" + "print(all_citations)" ] }, { @@ -2449,7 +1992,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 53, "metadata": {}, "outputs": [ { @@ -2457,17 +2000,7 @@ "output_type": "stream", "text": [ "Papers cited:\n", - " id citing_arxiv_id cited_arxiv_id \\\n", - "2 5 1810.04805v2 1802.05365v2 \n", - "7 10 1810.04805v2 1706.03762v7 \n", - "\n", - " bib_title bib_key \\\n", - "2 Deep contextualized word representations peters2018deep \n", - "7 attention is all you need something \n", - "\n", - " citing_sections citing_paragraphs \n", - "2 [\"related_work\"] [] \n", - "7 \"citing_sections\" [] \n" + "[(1, '1810.04805v2', '1706.03762v7', 'attention is all you need', 'something', None, ['something'], [])]\n" ] } ], @@ -2487,7 +2020,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 54, "metadata": {}, "outputs": [ { @@ -2495,13 +2028,7 @@ "output_type": "stream", "text": [ "Papers that cite:\n", - " id citing_arxiv_id cited_arxiv_id bib_title \\\n", - "3 6 2010.11929v2 1706.03762v7 Attention Is All You Need \n", - "7 10 1810.04805v2 1706.03762v7 attention is all you need \n", - "\n", - " bib_key citing_sections citing_paragraphs \n", - "3 vaswani2017attention [\"introduction\", \"model\"] [] \n", - "7 something \"citing_sections\" [] \n" + "[(1, '1810.04805v2', '1706.03762v7', 'attention is all you need', 'something', None, ['something'], [])]\n" ] } ], @@ -2521,7 +2048,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 55, "metadata": {}, "outputs": [ { @@ -2558,16 +2085,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 56, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Linked author ss_ashish_vaswani (position 1)\n", - "Linked author ss_noam_shazeer (position 2)\n", - "Linked author ss_niki_parmar (position 3)\n" + "ename": "UndefinedObject", + "evalue": "constraint \"ux_arxiv_paper_authors_unique\" for table \"arxiv_paper_authors\" does not exist\n", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mUndefinedObject\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[56]\u001b[39m\u001b[32m, line 7\u001b[39m\n\u001b[32m 1\u001b[39m paper_authors = [\n\u001b[32m 2\u001b[39m {\u001b[33m'\u001b[39m\u001b[33mpaper_arxiv_id\u001b[39m\u001b[33m'\u001b[39m: \u001b[33m'\u001b[39m\u001b[33m1706.03762v7\u001b[39m\u001b[33m'\u001b[39m, \u001b[33m'\u001b[39m\u001b[33mauthor_id\u001b[39m\u001b[33m'\u001b[39m: \u001b[33m'\u001b[39m\u001b[33mss_ashish_vaswani\u001b[39m\u001b[33m'\u001b[39m, \u001b[33m'\u001b[39m\u001b[33mauthor_sequence\u001b[39m\u001b[33m'\u001b[39m: \u001b[32m1\u001b[39m},\n\u001b[32m 3\u001b[39m {\u001b[33m'\u001b[39m\u001b[33mpaper_arxiv_id\u001b[39m\u001b[33m'\u001b[39m: \u001b[33m'\u001b[39m\u001b[33m1706.03762v7\u001b[39m\u001b[33m'\u001b[39m, \u001b[33m'\u001b[39m\u001b[33mauthor_id\u001b[39m\u001b[33m'\u001b[39m: \u001b[33m'\u001b[39m\u001b[33mss_noam_shazeer\u001b[39m\u001b[33m'\u001b[39m, \u001b[33m'\u001b[39m\u001b[33mauthor_sequence\u001b[39m\u001b[33m'\u001b[39m: \u001b[32m2\u001b[39m},\n\u001b[32m 4\u001b[39m {\u001b[33m'\u001b[39m\u001b[33mpaper_arxiv_id\u001b[39m\u001b[33m'\u001b[39m: \u001b[33m'\u001b[39m\u001b[33m1706.03762v7\u001b[39m\u001b[33m'\u001b[39m, \u001b[33m'\u001b[39m\u001b[33mauthor_id\u001b[39m\u001b[33m'\u001b[39m: \u001b[33m'\u001b[39m\u001b[33mss_niki_parmar\u001b[39m\u001b[33m'\u001b[39m, \u001b[33m'\u001b[39m\u001b[33mauthor_sequence\u001b[39m\u001b[33m'\u001b[39m: \u001b[32m3\u001b[39m}\n\u001b[32m 5\u001b[39m ]\n\u001b[32m 6\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m relation \u001b[38;5;129;01min\u001b[39;00m paper_authors:\n\u001b[32m----> \u001b[39m\u001b[32m7\u001b[39m \u001b[43mresearch_arcade\u001b[49m\u001b[43m.\u001b[49m\u001b[43minsert_edge\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43marxiv_paper_author\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43medge_features\u001b[49m\u001b[43m=\u001b[49m\u001b[43mrelation\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 8\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mLinked author \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mrelation[\u001b[33m'\u001b[39m\u001b[33mauthor_id\u001b[39m\u001b[33m'\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m (position \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mrelation[\u001b[33m'\u001b[39m\u001b[33mauthor_sequence\u001b[39m\u001b[33m'\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m)\u001b[39m\u001b[33m\"\u001b[39m)\n", + "\u001b[36mFile \u001b[39m\u001b[32m/data/cl195/paper-crawler/research_arcade/research_arcade.py:283\u001b[39m, in \u001b[36mResearchArcade.insert_edge\u001b[39m\u001b[34m(self, table, edge_features)\u001b[39m\n\u001b[32m 281\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.arxiv_citation.insert_citation(**edge_features)\n\u001b[32m 282\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m table == \u001b[33m'\u001b[39m\u001b[33marxiv_paper_author\u001b[39m\u001b[33m'\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m283\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43marxiv_paper_author\u001b[49m\u001b[43m.\u001b[49m\u001b[43minsert_paper_author\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43medge_features\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 284\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m table == \u001b[33m'\u001b[39m\u001b[33marxiv_paper_category\u001b[39m\u001b[33m'\u001b[39m:\n\u001b[32m 285\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.arxiv_paper_category.insert_paper_category(**edge_features)\n", + "\u001b[36mFile \u001b[39m\u001b[32m/data/cl195/paper-crawler/research_arcade/sql_database/sql_arxiv_paper_authors.py:72\u001b[39m, in \u001b[36mSQLArxivPaperAuthor.insert_paper_author\u001b[39m\u001b[34m(self, paper_arxiv_id, author_id, author_sequence)\u001b[39m\n\u001b[32m 70\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m 71\u001b[39m cur = conn.cursor()\n\u001b[32m---> \u001b[39m\u001b[32m72\u001b[39m \u001b[43mcur\u001b[49m\u001b[43m.\u001b[49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 73\u001b[39m \u001b[38;5;250;43m \u001b[39;49m\u001b[33;43;03m\"\"\"\u001b[39;49;00m\n\u001b[32m 74\u001b[39m \u001b[33;43;03m INSERT INTO arxiv_paper_authors (paper_arxiv_id, author_id, author_sequence)\u001b[39;49;00m\n\u001b[32m 75\u001b[39m \u001b[33;43;03m VALUES (%s, %s, %s)\u001b[39;49;00m\n\u001b[32m 76\u001b[39m \u001b[33;43;03m ON CONFLICT ON CONSTRAINT ux_arxiv_paper_authors_unique DO NOTHING\u001b[39;49;00m\n\u001b[32m 77\u001b[39m \u001b[33;43;03m \"\"\"\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m 78\u001b[39m \u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mpaper_arxiv_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mauthor_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mauthor_sequence\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 79\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 80\u001b[39m inserted = cur.rowcount > \u001b[32m0\u001b[39m \u001b[38;5;66;03m# 1 if inserted, 0 if conflict\u001b[39;00m\n\u001b[32m 81\u001b[39m cur.close()\n", + "\u001b[31mUndefinedObject\u001b[39m: constraint \"ux_arxiv_paper_authors_unique\" for table \"arxiv_paper_authors\" does not exist\n" ] } ], @@ -2591,14 +2122,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 57, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Successfully imported 16 paper-author relationships from ./examples/csv_data/csv_arxiv_paper_author_example.csv\n" + "Error: CSV file ./examples/csv_data/csv_arxiv_paper_author_example.csv does not exist.\n" ] } ], @@ -2641,32 +2172,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 59, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total relationships: 35\n", - " paper_arxiv_id author_id author_sequence\n", - "0 1706.03762v7 ss_noam_shazeer 2\n", - "1 1706.03762v7 ss_niki_parmar 3\n", - "2 1706.03762v7 1234567 1\n", - "3 1706.03762v7 2345678 2\n", - "4 1706.03762v7 3456789 3\n", - "5 1706.03762v7 4567890 4\n", - "6 1706.03762v7 5678901 5\n", - "7 1706.03762v7 6789012 6\n", - "8 1706.03762v7 7890123 7\n", - "9 1706.03762v7 8901234 8\n" + "ename": "TypeError", + "evalue": "object of type 'NoneType' has no len()", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mTypeError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[59]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m 1\u001b[39m all_relations = research_arcade.get_all_edge_features(\u001b[33m\"\u001b[39m\u001b[33marxiv_paper_author\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mTotal relationships: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mall_relations\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n\u001b[32m 3\u001b[39m \u001b[38;5;28mprint\u001b[39m(all_relations)\n", + "\u001b[31mTypeError\u001b[39m: object of type 'NoneType' has no len()" ] } ], "source": [ "all_relations = research_arcade.get_all_edge_features(\"arxiv_paper_author\")\n", "print(f\"Total relationships: {len(all_relations)}\")\n", - "print(all_relations.head(10))" + "print(all_relations)" ] }, { @@ -2678,7 +2202,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 61, "metadata": {}, "outputs": [ { @@ -2686,26 +2210,7 @@ "output_type": "stream", "text": [ "Authors:\n", - " paper_arxiv_id author_id author_sequence\n", - "0 1706.03762v7 1234567 1\n", - "1 1706.03762v7 ss_ashish_vaswani 1\n", - "2 1706.03762v7 1234567 1\n", - "3 1706.03762v7 ss_noam_shazeer 2\n", - "4 1706.03762v7 2345678 2\n", - "5 1706.03762v7 2345678 2\n", - "6 1706.03762v7 ss_niki_parmar 3\n", - "7 1706.03762v7 3456789 3\n", - "8 1706.03762v7 3456789 3\n", - "10 1706.03762v7 4567890 4\n", - "9 1706.03762v7 4567890 4\n", - "11 1706.03762v7 5678901 5\n", - "12 1706.03762v7 5678901 5\n", - "13 1706.03762v7 6789012 6\n", - "14 1706.03762v7 6789012 6\n", - "15 1706.03762v7 7890123 7\n", - "16 1706.03762v7 7890123 7\n", - "17 1706.03762v7 8901234 8\n", - "18 1706.03762v7 8901234 8\n" + "None\n" ] } ], @@ -2713,7 +2218,7 @@ "paper_id = {'paper_arxiv_id': '1706.03762v7'}\n", "authors = research_arcade.get_neighborhood(\"arxiv_paper_author\", primary_key=paper_id)\n", "print(\"Authors:\")\n", - "print(authors.sort_values('author_sequence'))" + "print(authors)" ] }, { @@ -2725,7 +2230,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 62, "metadata": {}, "outputs": [ { @@ -2733,8 +2238,7 @@ "output_type": "stream", "text": [ "Papers by author:\n", - " paper_arxiv_id author_id author_sequence\n", - "0 1706.03762v7 ss_ashish_vaswani 1\n" + "None\n" ] } ], @@ -2754,7 +2258,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 63, "metadata": {}, "outputs": [ { @@ -2787,16 +2291,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 64, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Linked 1\n", - "Linked 1\n", - "Linked 2\n" + "ename": "UndefinedObject", + "evalue": "constraint \"ux_arxiv_paper_category_unique\" for table \"arxiv_paper_category\" does not exist\n", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mUndefinedObject\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[64]\u001b[39m\u001b[32m, line 7\u001b[39m\n\u001b[32m 1\u001b[39m paper_categories = [\n\u001b[32m 2\u001b[39m {\u001b[33m'\u001b[39m\u001b[33mpaper_arxiv_id\u001b[39m\u001b[33m'\u001b[39m: \u001b[33m'\u001b[39m\u001b[33m1706.03762v7\u001b[39m\u001b[33m'\u001b[39m, \u001b[33m'\u001b[39m\u001b[33mcategory_id\u001b[39m\u001b[33m'\u001b[39m: \u001b[33m'\u001b[39m\u001b[33m1\u001b[39m\u001b[33m'\u001b[39m},\n\u001b[32m 3\u001b[39m {\u001b[33m'\u001b[39m\u001b[33mpaper_arxiv_id\u001b[39m\u001b[33m'\u001b[39m: \u001b[33m'\u001b[39m\u001b[33m1706.03762v7\u001b[39m\u001b[33m'\u001b[39m, \u001b[33m'\u001b[39m\u001b[33mcategory_id\u001b[39m\u001b[33m'\u001b[39m: \u001b[33m'\u001b[39m\u001b[33m1\u001b[39m\u001b[33m'\u001b[39m},\n\u001b[32m 4\u001b[39m {\u001b[33m'\u001b[39m\u001b[33mpaper_arxiv_id\u001b[39m\u001b[33m'\u001b[39m: \u001b[33m'\u001b[39m\u001b[33m1706.03762v7\u001b[39m\u001b[33m'\u001b[39m, \u001b[33m'\u001b[39m\u001b[33mcategory_id\u001b[39m\u001b[33m'\u001b[39m: \u001b[33m'\u001b[39m\u001b[33m2\u001b[39m\u001b[33m'\u001b[39m}\n\u001b[32m 5\u001b[39m ]\n\u001b[32m 6\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m relation \u001b[38;5;129;01min\u001b[39;00m paper_categories:\n\u001b[32m----> \u001b[39m\u001b[32m7\u001b[39m \u001b[43mresearch_arcade\u001b[49m\u001b[43m.\u001b[49m\u001b[43minsert_edge\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43marxiv_paper_category\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43medge_features\u001b[49m\u001b[43m=\u001b[49m\u001b[43mrelation\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 8\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mLinked \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mrelation[\u001b[33m'\u001b[39m\u001b[33mcategory_id\u001b[39m\u001b[33m'\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n", + "\u001b[36mFile \u001b[39m\u001b[32m/data/cl195/paper-crawler/research_arcade/research_arcade.py:285\u001b[39m, in \u001b[36mResearchArcade.insert_edge\u001b[39m\u001b[34m(self, table, edge_features)\u001b[39m\n\u001b[32m 283\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.arxiv_paper_author.insert_paper_author(**edge_features)\n\u001b[32m 284\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m table == \u001b[33m'\u001b[39m\u001b[33marxiv_paper_category\u001b[39m\u001b[33m'\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m285\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43marxiv_paper_category\u001b[49m\u001b[43m.\u001b[49m\u001b[43minsert_paper_category\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43medge_features\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 286\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m table == \u001b[33m'\u001b[39m\u001b[33marxiv_paper_figure\u001b[39m\u001b[33m'\u001b[39m:\n\u001b[32m 287\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.arxiv_paper_figure.insert_paper_figure(**edge_features)\n", + "\u001b[36mFile \u001b[39m\u001b[32m/data/cl195/paper-crawler/research_arcade/sql_database/sql_arxiv_paper_categories.py:71\u001b[39m, in \u001b[36mSQLArxivPaperCategory.insert_paper_category\u001b[39m\u001b[34m(self, paper_arxiv_id, category_id)\u001b[39m\n\u001b[32m 69\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m 70\u001b[39m cur = conn.cursor()\n\u001b[32m---> \u001b[39m\u001b[32m71\u001b[39m \u001b[43mcur\u001b[49m\u001b[43m.\u001b[49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 72\u001b[39m \u001b[38;5;250;43m \u001b[39;49m\u001b[33;43;03m\"\"\"\u001b[39;49;00m\n\u001b[32m 73\u001b[39m \u001b[33;43;03m INSERT INTO arxiv_paper_category (paper_arxiv_id, category_id)\u001b[39;49;00m\n\u001b[32m 74\u001b[39m \u001b[33;43;03m VALUES (%s, %s)\u001b[39;49;00m\n\u001b[32m 75\u001b[39m \u001b[33;43;03m ON CONFLICT ON CONSTRAINT ux_arxiv_paper_category_unique DO NOTHING\u001b[39;49;00m\n\u001b[32m 76\u001b[39m \u001b[33;43;03m \"\"\"\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m 77\u001b[39m \u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mpaper_arxiv_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcategory_id\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 78\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 79\u001b[39m inserted = cur.rowcount > \u001b[32m0\u001b[39m \u001b[38;5;66;03m# rowcount == 1 if inserted, 0 if skipped due to conflict\u001b[39;00m\n\u001b[32m 80\u001b[39m cur.close()\n", + "\u001b[31mUndefinedObject\u001b[39m: constraint \"ux_arxiv_paper_category_unique\" for table \"arxiv_paper_category\" does not exist\n" ] } ], @@ -2820,14 +2328,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 65, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Successfully imported 1 paper-category relationships from ./examples/csv_data/csv_arxiv_paper_category_example.csv\n" + "Error: CSV file ./examples/csv_data/csv_arxiv_paper_category_example.csv does not exist.\n" ] } ], @@ -2845,14 +2353,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 66, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "No new paper-category relationships to import\n" + "Error: JSON file ./examples/json_data/json_arxiv_paper_category_example.json does not exist.\n" ] } ], @@ -2870,20 +2378,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 67, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total relationships: 17\n", - " paper_arxiv_id category_id\n", - "0 1706.03762v7 1\n", - "1 1706.03762v7 1\n", - "2 1706.03762v7 2\n", - "3 1706.03762v7 cs.CL\n", - "4 1706.03762v7 cs.LG\n" + "ename": "TypeError", + "evalue": "object of type 'NoneType' has no len()", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mTypeError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[67]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m 1\u001b[39m all_relations = research_arcade.get_all_edge_features(\u001b[33m\"\u001b[39m\u001b[33marxiv_paper_category\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mTotal relationships: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mall_relations\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n\u001b[32m 3\u001b[39m \u001b[38;5;28mprint\u001b[39m(all_relations.head())\n", + "\u001b[31mTypeError\u001b[39m: object of type 'NoneType' has no len()" ] } ], @@ -2902,7 +2408,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 68, "metadata": {}, "outputs": [ { @@ -2910,13 +2416,7 @@ "output_type": "stream", "text": [ "Categories:\n", - " paper_arxiv_id category_id\n", - "0 1706.03762v7 1\n", - "1 1706.03762v7 1\n", - "2 1706.03762v7 2\n", - "3 1706.03762v7 cs.CL\n", - "4 1706.03762v7 cs.LG\n", - "5 1706.03762v7 cs.AI\n" + "None\n" ] } ], @@ -2936,7 +2436,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 69, "metadata": {}, "outputs": [ { @@ -2944,12 +2444,7 @@ "output_type": "stream", "text": [ "Papers in category:\n", - " paper_arxiv_id category_id\n", - "0 1706.03762v7 cs.LG\n", - "1 1810.04805v2 cs.LG\n", - "2 1409.0473v7 cs.LG\n", - "3 1512.03385v1 cs.LG\n", - "4 2010.11929v2 cs.LG\n" + "None\n" ] } ], @@ -2969,7 +2464,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 70, "metadata": {}, "outputs": [ { @@ -3002,15 +2497,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 71, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Linked figure 1)\n", - "Linked figure 2)\n" + "ename": "UndefinedObject", + "evalue": "constraint \"ux_arxiv_paper_figures_unique\" for table \"arxiv_paper_figures\" does not exist\n", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mUndefinedObject\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[71]\u001b[39m\u001b[32m, line 6\u001b[39m\n\u001b[32m 1\u001b[39m paper_figures = [\n\u001b[32m 2\u001b[39m {\u001b[33m'\u001b[39m\u001b[33mpaper_arxiv_id\u001b[39m\u001b[33m'\u001b[39m: \u001b[33m'\u001b[39m\u001b[33m1706.03762v7\u001b[39m\u001b[33m'\u001b[39m, \u001b[33m'\u001b[39m\u001b[33mfigure_id\u001b[39m\u001b[33m'\u001b[39m: \u001b[32m1\u001b[39m},\n\u001b[32m 3\u001b[39m {\u001b[33m'\u001b[39m\u001b[33mpaper_arxiv_id\u001b[39m\u001b[33m'\u001b[39m: \u001b[33m'\u001b[39m\u001b[33m1706.03762v7\u001b[39m\u001b[33m'\u001b[39m, \u001b[33m'\u001b[39m\u001b[33mfigure_id\u001b[39m\u001b[33m'\u001b[39m: \u001b[32m2\u001b[39m}\n\u001b[32m 4\u001b[39m ]\n\u001b[32m 5\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m relation \u001b[38;5;129;01min\u001b[39;00m paper_figures:\n\u001b[32m----> \u001b[39m\u001b[32m6\u001b[39m \u001b[43mresearch_arcade\u001b[49m\u001b[43m.\u001b[49m\u001b[43minsert_edge\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43marxiv_paper_figure\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43medge_features\u001b[49m\u001b[43m=\u001b[49m\u001b[43mrelation\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 7\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mLinked figure \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mrelation[\u001b[33m'\u001b[39m\u001b[33mfigure_id\u001b[39m\u001b[33m'\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m)\u001b[39m\u001b[33m\"\u001b[39m)\n", + "\u001b[36mFile \u001b[39m\u001b[32m/data/cl195/paper-crawler/research_arcade/research_arcade.py:287\u001b[39m, in \u001b[36mResearchArcade.insert_edge\u001b[39m\u001b[34m(self, table, edge_features)\u001b[39m\n\u001b[32m 285\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.arxiv_paper_category.insert_paper_category(**edge_features)\n\u001b[32m 286\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m table == \u001b[33m'\u001b[39m\u001b[33marxiv_paper_figure\u001b[39m\u001b[33m'\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m287\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43marxiv_paper_figure\u001b[49m\u001b[43m.\u001b[49m\u001b[43minsert_paper_figure\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43medge_features\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 288\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m table == \u001b[33m'\u001b[39m\u001b[33marxiv_paper_table\u001b[39m\u001b[33m'\u001b[39m:\n\u001b[32m 289\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.arxiv_paper_table.insert_paper_table(**edge_features)\n", + "\u001b[36mFile \u001b[39m\u001b[32m/data/cl195/paper-crawler/research_arcade/sql_database/sql_arxiv_paper_figures.py:71\u001b[39m, in \u001b[36mSQLArxivPaperFigure.insert_paper_figure\u001b[39m\u001b[34m(self, paper_arxiv_id, figure_id)\u001b[39m\n\u001b[32m 69\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m 70\u001b[39m cur = conn.cursor()\n\u001b[32m---> \u001b[39m\u001b[32m71\u001b[39m \u001b[43mcur\u001b[49m\u001b[43m.\u001b[49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 72\u001b[39m \u001b[38;5;250;43m \u001b[39;49m\u001b[33;43;03m\"\"\"\u001b[39;49;00m\n\u001b[32m 73\u001b[39m \u001b[33;43;03m INSERT INTO arxiv_paper_figures (paper_arxiv_id, figure_id)\u001b[39;49;00m\n\u001b[32m 74\u001b[39m \u001b[33;43;03m VALUES (%s, %s)\u001b[39;49;00m\n\u001b[32m 75\u001b[39m \u001b[33;43;03m ON CONFLICT ON CONSTRAINT ux_arxiv_paper_figures_unique DO NOTHING\u001b[39;49;00m\n\u001b[32m 76\u001b[39m \u001b[33;43;03m \"\"\"\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m 77\u001b[39m \u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mpaper_arxiv_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfigure_id\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 78\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 79\u001b[39m inserted = cur.rowcount > \u001b[32m0\u001b[39m\n\u001b[32m 80\u001b[39m cur.close()\n", + "\u001b[31mUndefinedObject\u001b[39m: constraint \"ux_arxiv_paper_figures_unique\" for table \"arxiv_paper_figures\" does not exist\n" ] } ], From af80c767435c2661e20c545eb61fa6c0a5969ef4 Mon Sep 17 00:00:00 2001 From: Chongshan0Lin Date: Sun, 9 Nov 2025 16:28:32 -0600 Subject: [PATCH 04/11] Added continuous crawling services and docker, to be tested --- docker/Dockerfile | 25 + docker/docker-compose.yml | 69 +++ requirements.txt | 4 + research_arcade/arxiv_crawler/crawler.py | 0 research_arcade/arxiv_crawler/processor.py | 0 .../arxiv_crawler/requirements.txt | 0 research_arcade/arxiv_utils/arxiv_crawler.py | 63 --- .../arxiv_utils/arxiv_id_crawler.py | 111 ++++ .../csv_database/csv_arxiv_paragraphs.py | 2 +- research_arcade/research_arcade.py | 14 +- research_arcade/services/__init__.py | 4 + research_arcade/services/__main__.py | 5 + research_arcade/services/config.py | 0 .../services/continuous_crawler.py | 150 ++++++ research_arcade/services/scheduler.py | 68 +++ .../sql_database/sql_arxiv_authors.py | 38 +- .../sql_arxiv_paper_categories.py | 9 +- .../sql_database/sql_arxiv_paper_figures.py | 12 +- .../sql_database/sql_arxiv_paper_tables.py | 74 ++- .../sql_arxiv_paragraph_references.py | 2 +- .../sql_database/sql_arxiv_sections.py | 2 +- ...rcade_complete_tutorial_with_imports.ipynb | 499 ++++++++---------- scripts/run_crawler_service.py | 0 23 files changed, 749 insertions(+), 402 deletions(-) create mode 100644 docker/Dockerfile create mode 100644 docker/docker-compose.yml create mode 100644 research_arcade/arxiv_crawler/crawler.py create mode 100644 research_arcade/arxiv_crawler/processor.py create mode 100644 research_arcade/arxiv_crawler/requirements.txt delete mode 100644 research_arcade/arxiv_utils/arxiv_crawler.py create mode 100644 research_arcade/arxiv_utils/arxiv_id_crawler.py create mode 100644 research_arcade/services/__init__.py create mode 100644 research_arcade/services/__main__.py create mode 100644 research_arcade/services/config.py create mode 100644 research_arcade/services/continuous_crawler.py create mode 100644 research_arcade/services/scheduler.py create mode 100644 scripts/run_crawler_service.py diff --git a/docker/Dockerfile b/docker/Dockerfile new file mode 100644 index 0000000..593ec3a --- /dev/null +++ b/docker/Dockerfile @@ -0,0 +1,25 @@ +FROM python:3.11-slim + +WORKDIR /app + +# Install system dependencies if needed +RUN apt-get update && apt-get install -y \ + gcc \ + g++ \ + git \ + && rm -rf /var/lib/apt/lists/* + +# Copy the entire project +COPY . . + +# Install the package and dependencies +RUN pip install --no-cache-dir -e . + +# Create directories for data and logs +RUN mkdir -p /app/download /app/logs + +# Set environment variables +ENV PYTHONUNBUFFERED=1 + +# Run the scheduler service +CMD ["python", "-m", "research_arcade.services.scheduler"] \ No newline at end of file diff --git a/docker/docker-compose.yml b/docker/docker-compose.yml new file mode 100644 index 0000000..4faa4a5 --- /dev/null +++ b/docker/docker-compose.yml @@ -0,0 +1,69 @@ +version: '3.8' + +services: + crawler-service: + build: + context: .. + dockerfile: docker/Dockerfile + container_name: research-arcade-crawler + restart: unless-stopped + environment: + # Database configuration + - DB_TYPE=${DB_TYPE:-csv} + - CSV_DATASET_FOLDER_PATH=${CSV_DATASET_FOLDER_PATH:-/app/data/csv} + + # SQL configuration (if using SQL) + - HOST=${HOST:-postgres} + - PORT=${PORT:-5432} + - DBNAME=${DBNAME:-research_arcade} + - USER=${USER:-postgres} + - PASSWORD=${PASSWORD} + + # Crawler configuration + - DOWNLOAD_PATH=${DOWNLOAD_PATH:-/app/download} + - CRAWL_INTERVAL=${CRAWL_INTERVAL:-5} + + # Scheduler configuration + - SCHEDULE_TYPE=${SCHEDULE_TYPE:-interval} + - RUN_EVERY_DAYS=${RUN_EVERY_DAYS:-5} + - CRAWL_HOUR=${CRAWL_HOUR:-2} + - CRAWL_MINUTE=${CRAWL_MINUTE:-0} + - RUN_ON_STARTUP=${RUN_ON_STARTUP:-true} + volumes: + # Persist downloaded papers + - ./data/download:/app/download + # Persist CSV data (if using CSV) + - ./data/csv:/app/data/csv + # Persist logs + - ./logs:/app/logs + depends_on: + - postgres + networks: + - research-arcade-network + + postgres: + image: postgres:15-alpine + container_name: research-arcade-db + restart: unless-stopped + environment: + - POSTGRES_DB=${DBNAME:-research_arcade} + - POSTGRES_USER=${USER:-postgres} + - POSTGRES_PASSWORD=${PASSWORD} + volumes: + - postgres_data:/var/lib/postgresql/data + ports: + - "${PORT:-5432}:5432" + networks: + - research-arcade-network + healthcheck: + test: ["CMD-SHELL", "pg_isready -U ${USER:-postgres}"] + interval: 10s + timeout: 5s + retries: 5 + +volumes: + postgres_data: + +networks: + research-arcade-network: + driver: bridge \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index 3357af0..c8357bc 100644 --- a/requirements.txt +++ b/requirements.txt @@ -7,3 +7,7 @@ pdfminer_six==20250506 psycopg2==2.9.10 Requests==2.32.5 tqdm==4.67.1 +APScheduler>=3.10.0 +python-dotenv>=1.0.0 +psycopg2-binary>=2.9.0 # if using PostgreSQL +requests>=2.31.0 diff --git a/research_arcade/arxiv_crawler/crawler.py b/research_arcade/arxiv_crawler/crawler.py new file mode 100644 index 0000000..e69de29 diff --git a/research_arcade/arxiv_crawler/processor.py b/research_arcade/arxiv_crawler/processor.py new file mode 100644 index 0000000..e69de29 diff --git a/research_arcade/arxiv_crawler/requirements.txt b/research_arcade/arxiv_crawler/requirements.txt new file mode 100644 index 0000000..e69de29 diff --git a/research_arcade/arxiv_utils/arxiv_crawler.py b/research_arcade/arxiv_utils/arxiv_crawler.py deleted file mode 100644 index 0d05353..0000000 --- a/research_arcade/arxiv_utils/arxiv_crawler.py +++ /dev/null @@ -1,63 +0,0 @@ -import sys -import os - -sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) - -from ..arxiv_utils.paper_crawler.crawler_job import CrawlerJob - - -class ArxivCrawler: - def __init__(self, dest_dir): - self.cj = CrawlerJob(dest_dir=dest_dir) - - def crawl_paper_by_time(self, start_date, end_date, dest_path): - - arxiv_ids = self.cj.crawl_recent_arxiv_paper_new(start_date=start_date, end_date=end_date, path=dest_path) - - return arxiv_ids - - # Wait a second... is this method sql based or csv based? - def download_papers(self, arxiv_ids): - self.cj.download_papers(arxiv_ids=arxiv_ids) - - def process_paper_graphs(self, arxiv_ids): - self.cj.process_paper_graphs(arxiv_ids=arxiv_ids) - - def process_paper_paragraphs(self, arxiv_ids): - self.cj.process_paper_paragraphs(arxiv_ids=arxiv_ids) - - def crawl_paper_data_from_api(self, arxiv_ids): - pass - - def crawl_section_data_from_api(self, arxiv_ids): - pass - - def crawl_paragraph_data_from_api(self, arxiv_ids): - # Process first then tackle down the problems - pass - - - def crawl_category_data_from_api(self, arxiv_ids): - # Give the category of data - pass - - def crawl_paper_category_data_from_api(self, arxiv_ids): - # Process first then tackle down the problems - pass - - def crawl_author_data_from_api(self, arxiv_ids): - # Given the arxiv ids, search the authors on semantic scholar - # This also returns the edge information? Edge between papers and authors - pass - - def crawl_figure_data_from_api(self, arxiv_ids): - - pass - - def crawl_table_data_from_api(self, arxiv_ids): - pass - - def crawl_paragraph_references_data_from_api(self, arxiv_ids): - pass - - diff --git a/research_arcade/arxiv_utils/arxiv_id_crawler.py b/research_arcade/arxiv_utils/arxiv_id_crawler.py new file mode 100644 index 0000000..2f87c51 --- /dev/null +++ b/research_arcade/arxiv_utils/arxiv_id_crawler.py @@ -0,0 +1,111 @@ +import sys +import os +sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) + +import argparse +import json +from datetime import date, datetime +from pathlib import Path +from typing import List + +from paperscraper.get_dumps import arxiv +from multi_input.multi_input import MultiInput # Adjust import path as needed + + +def valid_date(s: str) -> str: + # Accept YYYY-MM-DD or YYYY-MM; store as YYYY-MM-DD + for fmt in ("%Y-%m-%d", "%Y-%m"): + try: + d = datetime.strptime(s, fmt) + if fmt == "%Y-%m": + d = d.replace(day=1) + return d.date().isoformat() + except ValueError: + pass + raise argparse.ArgumentTypeError("Use YYYY-MM-DD or YYYY-MM") + + +def extract_arxiv_ids(file_path: str) -> List[str]: + mi = MultiInput() + arxiv_ids: List[str] = [] + seen = set() + + def try_add(doi): + if not doi: + return + try: + arx_id = mi.doi_to_id(doi) + except Exception: + return + if arx_id not in seen: + seen.add(arx_id) + arxiv_ids.append(arx_id) + + with open(file_path, "r", encoding="utf-8") as f: + # Peek first non-whitespace char to detect JSON array vs NDJSON + start = f.read(1) + while start and start.isspace(): + start = f.read(1) + f.seek(0) + + if start == "[": # JSON array + data = json.load(f) + for obj in data: + if isinstance(obj, dict): + doi = obj.get("doi") or obj.get("DOI") or obj.get("paper_doi") + try_add(doi) + else: # NDJSON + for lineno, line in enumerate(f, 1): + line = line.strip() + if not line: + continue + try: + obj = json.loads(line) + except json.JSONDecodeError: + # skip malformed line + continue + if isinstance(obj, dict): + doi = obj.get("doi") or obj.get("DOI") or obj.get("paper_doi") + try_add(doi) + + return arxiv_ids + +# [start_date, end_date], both ends inclusive +def arxiv_id_crawler(start_date, end_date, save_path="."): + save_path = f"{save_path}/arxiv_metadata_{start_date}_{end_date}.jsonl" + + arxiv(start_date=start_date, end_date=end_date, save_path=save_path) + arxiv_ids = extract_arxiv_ids(file_path=save_path) + + return arxiv_ids, save_path + + + + +# Testing passed! + +# def main(): +# parser = argparse.ArgumentParser(description="Crawl arXiv papers by date range") +# parser.add_argument("--start-date", type=valid_date, required=True, +# help="Start date (YYYY-MM-DD or YYYY-MM)") +# parser.add_argument("--end-date", type=valid_date, required=True, +# help="End date (YYYY-MM-DD or YYYY-MM)") +# parser.add_argument("--field", type=str, help="Field filter (optional)") +# parser.add_argument("--save-path", type=str, default="./data", +# help="Directory to save results") + +# args = parser.parse_args() + +# arxiv_ids, output_file = arxiv_id_crawler( +# start_date=args.start_date, +# end_date=args.end_date, +# field=args.field, +# save_path=args.save_path +# ) + +# print(f"Extracted {len(arxiv_ids)} arXiv IDs") +# print(f"Saved to: {output_file}") + + +# if __name__ == "__main__": +# main() \ No newline at end of file diff --git a/research_arcade/csv_database/csv_arxiv_paragraphs.py b/research_arcade/csv_database/csv_arxiv_paragraphs.py index 07bae77..2bca238 100644 --- a/research_arcade/csv_database/csv_arxiv_paragraphs.py +++ b/research_arcade/csv_database/csv_arxiv_paragraphs.py @@ -215,7 +215,7 @@ def construct_paragraphs_table_from_api(self, arxiv_ids, dest_dir): figures_dir_path = f"{dest_dir}/output/images" output_dir_path = f"{dest_dir}/output/paragraphs" pgp = PaperGraphProcessor(data_dir=data_dir_path, figures_dir=figures_dir_path, output_dir=output_dir_path) - + papers = [] for arxiv_id in arxiv_ids: paper_dir = f"{dest_dir}/{arxiv_id}/{arxiv_id}_metadata.json" diff --git a/research_arcade/research_arcade.py b/research_arcade/research_arcade.py index 9921a18..433876c 100644 --- a/research_arcade/research_arcade.py +++ b/research_arcade/research_arcade.py @@ -213,7 +213,7 @@ def get_node_features_by_id(self, table: str, primary_key: dict) -> Optional[pd. return self.openreview_paragraphs.get_paragraphs_by_paper_id(**primary_key) # Tables in arxiv dataset elif table == 'arxiv_authors': - return self.arxiv_authors.get_author_by_id(**primary_key) + return self.arxiv_authors.get_author_by_semantic_scholar_id(**primary_key) elif table == 'arxiv_categories': return self.arxiv_categories.get_category_by_id(**primary_key) elif table == 'arxiv_figures': @@ -583,8 +583,6 @@ def construct_table_from_api(self, table: str, config: dict) -> Optional[pd.Data self.arxiv_sections.construct_sections_table_from_api(**config) elif table == "arxiv_paragraphs": self.arxiv_paragraphs.construct_paragraphs_table_from_api(**config) - elif table == "arxiv_categories": - self.arxiv_categories.construct_category_table_from_api(**config) elif table == "arxiv_paper_authors": self.arxiv_paper_authors.construct_papers_table_from_api(**config) elif table == "arxiv_paper_figures": @@ -599,7 +597,14 @@ def construct_table_from_api(self, table: str, config: dict) -> Optional[pd.Data self.arxiv_paragraph_references.construct_papers_table_from_api(**config) else: print(f"Table {table} does not support construction from API") - + + + def construct_arxiv_tables_from_api(self, config: dict) -> Optional[pd.DataFrame]: + table_names = ["arxiv_papers", "arxiv_authors", "arxiv_categories", "arxiv_sections", "arxiv_figures", "arxiv_tables", "arxiv_paper_authors", "arxiv_paper_figures", "arxiv_paper_tables", "arxiv_paper_categories", "arxiv_citations", "arxiv_paragraphs", "arxiv_paragraph_references"] + + + for table_name in table_names: + self.construct_arxiv_tables_from_api(table= table_name, config=config) @@ -708,3 +713,4 @@ def construct_table_from_json(self, table: str, config: dict) -> Optional[pd.Dat self.arxiv_paragraph_reference.construct_table_from_json(**config) else: print(f"Table {table} does not support construction from JSON") + diff --git a/research_arcade/services/__init__.py b/research_arcade/services/__init__.py new file mode 100644 index 0000000..552e3e3 --- /dev/null +++ b/research_arcade/services/__init__.py @@ -0,0 +1,4 @@ +from .continuous_crawler import ContinuousCrawlerService +from .scheduler import main as run_scheduler + +__all__ = ['ContinuousCrawlerService', 'run_scheduler'] \ No newline at end of file diff --git a/research_arcade/services/__main__.py b/research_arcade/services/__main__.py new file mode 100644 index 0000000..fbebc94 --- /dev/null +++ b/research_arcade/services/__main__.py @@ -0,0 +1,5 @@ +"""Entry point for running the scheduler as a module.""" +from .scheduler import main + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/research_arcade/services/config.py b/research_arcade/services/config.py new file mode 100644 index 0000000..e69de29 diff --git a/research_arcade/services/continuous_crawler.py b/research_arcade/services/continuous_crawler.py new file mode 100644 index 0000000..ac06db9 --- /dev/null +++ b/research_arcade/services/continuous_crawler.py @@ -0,0 +1,150 @@ +import logging +from datetime import datetime, timedelta +from typing import Optional, Dict, List +import os +from dotenv import load_dotenv + +from ..arxiv_utils.arxiv_id_crawler import arxiv_id_crawler +from ..research_arcade import ResearchArcade + +logger = logging.getLogger(__name__) + +# Load environment variables +load_dotenv() + + +class ContinuousCrawlerService: + """Service for continuous arXiv paper crawling.""" + + def __init__(self, dest_path: str = "./download", db_type: str = "csv", crawl_interval: int = 7): + """ + Initialize the continuous crawler service. + + Args: + dest_path: Directory to download papers + db_type: Database type ('csv' or 'sql') + crawl_interval: Number of days to look back for papers + """ + # Length of crawling interval in days + self.crawl_interval = crawl_interval + + db_type = db_type.lower() + if db_type not in {"csv", "sql"}: + raise ValueError(f"Database type '{db_type}' not supported. Use 'csv' or 'sql'.") + + self.db_type = db_type + self.dest_path = dest_path + + # Initialize database configuration + self.config = self._load_config() + + # Track statistics + self.last_run: Optional[datetime] = None + + def _load_config(self) -> Dict: + """Load database configuration from environment variables.""" + if self.db_type == "csv": + csv_dir = os.getenv("CSV_DATASET_FOLDER_PATH") + if not csv_dir: + raise ValueError("CSV_DATASET_FOLDER_PATH not set in environment") + return {"csv_dir": csv_dir} + + elif self.db_type == "sql": + config = { + "host": os.getenv("HOST"), + "port": os.getenv("PORT"), + "dbname": os.getenv("DBNAME"), + "user": os.getenv("USER"), + "password": os.getenv("PASSWORD", None) # Optional + } + + missing = [k for k, v in config.items() if v is None and k != "password"] + if missing: + raise ValueError(f"Missing required SQL environment variables: {missing}") + + return config + + return {} + + def _calculate_date_range(self) -> tuple[str, str]: + + end_date = datetime.today() + start_date = end_date - timedelta(days=self.crawl_interval - 1) + + + return start_date.strftime("%Y-%m-%d"), end_date.strftime("%Y-%m-%d") + + def run_daily_crawl(self) -> Dict: + """Execute daily crawl and return statistics.""" + logger.info(f"Starting crawl at {datetime.now()}") + + try: + # Calculate date range + start_date, end_date = self._calculate_date_range() + logger.info(f"Crawling papers from {start_date} to {end_date}") + + # Get arXiv IDs for the date range + arxiv_ids = arxiv_id_crawler(start_date=start_date, end_date=end_date) + logger.info(f"Found {len(arxiv_ids)} papers") + + if not arxiv_ids: + logger.info("No papers found for the specified date range") + return { + 'total_found': 0, + 'processed': 0, + 'failed': 0, + 'failed_ids': [] + } + + # Initialize ResearchArcade + ra = ResearchArcade(db_type=self.db_type, config=self.config) + + # Prepare input for batch processing + input_config = { + "arxiv_ids": arxiv_ids, + "dest_dir": self.dest_path + } + + # Process papers + logger.info("Starting paper processing...") + result = ra.construct_arxiv_tables_from_api(config=input_config) + + # Update last run timestamp + self.last_run = datetime.now() + + success_count = len(arxiv_ids) # Assuming all succeed for now + failed_ids = [] # You may want to track failures in ResearchArcade + + + stats = { + 'timestamp': self.last_run.isoformat(), + 'date_range': {'start': start_date, 'end': end_date}, + 'total_found': len(arxiv_ids), + 'processed': success_count, + 'failed': len(failed_ids), + 'failed_ids': failed_ids + } + + logger.info(f"Crawl completed successfully: {stats}") + return stats + + except Exception as e: + logger.error(f"Daily crawl failed: {e}", exc_info=True) + raise + + def run_once(self) -> Dict: + """ + Run the crawler once immediately (useful for testing). + Alias for run_daily_crawl. + """ + return self.run_daily_crawl() + + def get_status(self) -> Dict: + """Get current status of the crawler service.""" + return { + 'db_type': self.db_type, + 'dest_path': self.dest_path, + 'crawl_interval': self.crawl_interval, + 'last_run': self.last_run.isoformat() if self.last_run else None, + 'config_loaded': bool(self.config) + } \ No newline at end of file diff --git a/research_arcade/services/scheduler.py b/research_arcade/services/scheduler.py new file mode 100644 index 0000000..1ff48f9 --- /dev/null +++ b/research_arcade/services/scheduler.py @@ -0,0 +1,68 @@ +import logging +import os +from apscheduler.schedulers.blocking import BlockingScheduler +from apscheduler.triggers.interval import IntervalTrigger # <-- Changed +from dotenv import load_dotenv + +from .continuous_crawler import ContinuousCrawlerService + +load_dotenv() + +logging.basicConfig( + level=logging.INFO, + format='%(asctime)s - %(name)s - %(levelname)s - %(message)s' +) +logger = logging.getLogger(__name__) + + +def main(): + """Run the scheduler for continuous arXiv crawling.""" + + # Get configuration from environment + db_type = os.getenv("DB_TYPE", "csv") + dest_path = os.getenv("DOWNLOAD_PATH", "./download") + crawl_interval = int(os.getenv("CRAWL_INTERVAL", "7")) + run_every_days = int(os.getenv("RUN_EVERY_DAYS", "5")) # <-- New + run_on_startup = os.getenv("RUN_ON_STARTUP", "false").lower() == "true" + + # Initialize the crawler service + logger.info("Initializing ContinuousCrawlerService...") + logger.info(f"Config: db_type={db_type}, crawl_interval={crawl_interval} days") + + service = ContinuousCrawlerService( + dest_path=dest_path, + db_type=db_type, + crawl_interval=crawl_interval + ) + + # Create scheduler + scheduler = BlockingScheduler() + + # Schedule to run every 5 days + scheduler.add_job( + service.run_daily_crawl, + trigger=IntervalTrigger(days=run_every_days), # <-- Run every 5 days + id='periodic_arxiv_crawl', + name=f'Periodic arXiv paper crawl (every {run_every_days} days)', + replace_existing=True + ) + + logger.info(f"Scheduler started. Will run every {run_every_days} days") + + # Run once on startup (recommended for interval-based scheduling) + if run_on_startup: + logger.info("Running initial crawl on startup...") + try: + stats = service.run_once() + logger.info(f"Initial crawl completed: {stats}") + except Exception as e: + logger.error(f"Initial crawl failed: {e}") + + try: + scheduler.start() + except (KeyboardInterrupt, SystemExit): + logger.info("Scheduler stopped.") + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/research_arcade/sql_database/sql_arxiv_authors.py b/research_arcade/sql_database/sql_arxiv_authors.py index 9d22c79..e04e4ef 100644 --- a/research_arcade/sql_database/sql_arxiv_authors.py +++ b/research_arcade/sql_database/sql_arxiv_authors.py @@ -38,7 +38,7 @@ def create_authors_table(self): try: cur = conn.cursor() cur.execute(""" - CREATE TABLE IF NOT EXISTS authors ( + CREATE TABLE IF NOT EXISTS arxiv_authors ( id SERIAL PRIMARY KEY, semantic_scholar_id VARCHAR(100) UNIQUE, name VARCHAR(255) NOT NULL, @@ -55,7 +55,7 @@ def insert_author(self, semantic_scholar_id, name, homepage=None): try: cur = conn.cursor() sql = """ - INSERT INTO authors (semantic_scholar_id, name, homepage) + INSERT INTO arxiv_authors (semantic_scholar_id, name, homepage) VALUES (%s, %s, %s) ON CONFLICT (semantic_scholar_id) DO NOTHING RETURNING id @@ -72,7 +72,7 @@ def delete_author_by_id(self, id: int) -> bool: conn = self._get_connection() try: cur = conn.cursor() - cur.execute("DELETE FROM authors WHERE id = %s RETURNING id", (id,)) + cur.execute("DELETE FROM arxiv_authors WHERE id = %s RETURNING id", (id,)) deleted = cur.fetchone() is not None cur.close() return deleted @@ -98,7 +98,7 @@ def update_author(self, id: int, semantic_scholar_id=None, name=None, homepage=N if not fields: return False # Nothing to update - sql = f"UPDATE authors SET {', '.join(fields)} WHERE id = %s RETURNING id" + sql = f"UPDATE arxiv_authors SET {', '.join(fields)} WHERE id = %s RETURNING id" values.append(id) conn = self._get_connection() @@ -121,7 +121,7 @@ def get_author_by_id(self, semantic_scholar: int, return_all=False): try: cur = conn.cursor() cur.execute( - "SELECT id, semantic_scholar_id, name, homepage FROM authors WHERE semantic_scholar = %s", + "SELECT id, semantic_scholar_id, name, homepage FROM arxiv_authors WHERE semantic_scholar = %s", (semantic_scholar,), ) res = cur.fetchall() if return_all else cur.fetchone() @@ -135,7 +135,7 @@ def check_author_exists(self, id: int) -> bool: conn = self._get_connection() try: cur = conn.cursor() - cur.execute("SELECT 1 FROM authors WHERE id = %s LIMIT 1", (id,)) + cur.execute("SELECT 1 FROM arxiv_authors WHERE id = %s LIMIT 1", (id,)) exists = cur.fetchone() is not None cur.close() return exists @@ -147,13 +147,13 @@ def get_all_authors(self, is_all_features=True): conn = self._get_connection() try: cur = conn.cursor() - cur.execute("SELECT id, semantic_scholar_id, name, homepage FROM authors") + cur.execute("SELECT id, semantic_scholar_id, name, homepage FROM arxiv_authors") rows = cur.fetchall() cur.close() return rows if rows else None finally: conn.close() - + def construct_authors_table_from_api(self, arxiv_ids, dest_dir): """ Given arxiv ids, find the semantic scholar ids and pages of the authors @@ -222,7 +222,7 @@ def construct_table_from_csv(self, csv_file): psycopg2.extras.execute_values( cur, """ - INSERT INTO authors (semantic_scholar_id, name, homepage) + INSERT INTO arxiv_authors (semantic_scholar_id, name, homepage) VALUES %s ON CONFLICT (semantic_scholar_id) DO NOTHING """, @@ -233,11 +233,11 @@ def construct_table_from_csv(self, csv_file): finally: conn.close() - print(f"Successfully imported {len(rows)} authors from {csv_file}") + print(f"Successfully imported {len(rows)} arxiv_authors from {csv_file}") return True except Exception as e: - print(f"Error importing authors from CSV: {e}") + print(f"Error importing arxiv_authors from CSV: {e}") return False def construct_table_from_json(self, json_file): @@ -322,7 +322,7 @@ def construct_table_from_json(self, json_file): psycopg2.extras.execute_values( cur, """ - INSERT INTO authors (semantic_scholar_id, name, homepage) + INSERT INTO arxiv_authors (semantic_scholar_id, name, homepage) VALUES %s ON CONFLICT (semantic_scholar_id) DO NOTHING """, @@ -333,7 +333,7 @@ def construct_table_from_json(self, json_file): finally: conn.close() - print(f"Successfully imported {len(rows)} authors from {json_file}") + print(f"Successfully imported {len(rows)} arxiv_authors from {json_file}") return True except json.JSONDecodeError as e: @@ -355,7 +355,7 @@ def get_author_by_semantic_scholar_id(self, semantic_scholar_id: str, return_all try: cur = conn.cursor() cur.execute( - "SELECT id, semantic_scholar_id, name, homepage FROM authors WHERE semantic_scholar_id = %s", + "SELECT id, semantic_scholar_id, name, homepage FROM arxiv_authors WHERE semantic_scholar_id = %s", (semantic_scholar_id,), ) res = cur.fetchall() if return_all else cur.fetchone() @@ -370,7 +370,7 @@ def check_author_exists_by_semantic_scholar_id(self, semantic_scholar_id: str) - conn = self._get_connection() try: cur = conn.cursor() - cur.execute("SELECT 1 FROM authors WHERE semantic_scholar_id = %s LIMIT 1", (semantic_scholar_id,)) + cur.execute("SELECT 1 FROM arxiv_authors WHERE semantic_scholar_id = %s LIMIT 1", (semantic_scholar_id,)) exists = cur.fetchone() is not None cur.close() return exists @@ -386,7 +386,7 @@ def get_author_id_by_semantic_scholar_id(self, semantic_scholar_id: str) -> int: conn = self._get_connection() try: cur = conn.cursor() - cur.execute("SELECT id FROM authors WHERE semantic_scholar_id = %s", (semantic_scholar_id,)) + cur.execute("SELECT id FROM arxiv_authors WHERE semantic_scholar_id = %s", (semantic_scholar_id,)) result = cur.fetchone() cur.close() return result[0] if result else None @@ -399,7 +399,7 @@ def delete_author_by_semantic_scholar_id(self, semantic_scholar_id: str) -> bool conn = self._get_connection() try: cur = conn.cursor() - cur.execute("DELETE FROM authors WHERE semantic_scholar_id = %s RETURNING id", (semantic_scholar_id,)) + cur.execute("DELETE FROM arxiv_authors WHERE semantic_scholar_id = %s RETURNING id", (semantic_scholar_id,)) deleted = cur.fetchone() is not None cur.close() return deleted @@ -424,9 +424,9 @@ def update_author_by_semantic_scholar_id(self, semantic_scholar_id: str, name=No if not fields: return False # Nothing to update - sql = f"UPDATE authors SET {', '.join(fields)} WHERE semantic_scholar_id = %s RETURNING id" + sql = f"UPDATE arxiv_authors SET {', '.join(fields)} WHERE semantic_scholar_id = %s RETURNING id" values.append(semantic_scholar_id) - + conn = self._get_connection() try: cur = conn.cursor() diff --git a/research_arcade/sql_database/sql_arxiv_paper_categories.py b/research_arcade/sql_database/sql_arxiv_paper_categories.py index cad7e0b..9a45f24 100644 --- a/research_arcade/sql_database/sql_arxiv_paper_categories.py +++ b/research_arcade/sql_database/sql_arxiv_paper_categories.py @@ -45,18 +45,13 @@ def create_paper_category_table(self): cur.execute(""" CREATE TABLE IF NOT EXISTS arxiv_paper_category ( paper_arxiv_id VARCHAR(100) NOT NULL, - category_id VARCHAR(100) NOT NULL + category_id VARCHAR(100) NOT NULL, + CONSTRAINT ux_arxiv_paper_category_unique UNIQUE (paper_arxiv_id, category_id) ) """) - # Composite unique index for preventing duplicates - cur.execute(""" - CREATE UNIQUE INDEX IF NOT EXISTS ux_arxiv_paper_category_unique - ON arxiv_paper_category (paper_arxiv_id, category_id) - """) cur.close() finally: conn.close() - # ------------------------- # Insert # ------------------------- diff --git a/research_arcade/sql_database/sql_arxiv_paper_figures.py b/research_arcade/sql_database/sql_arxiv_paper_figures.py index cf9a510..d5b2056 100644 --- a/research_arcade/sql_database/sql_arxiv_paper_figures.py +++ b/research_arcade/sql_database/sql_arxiv_paper_figures.py @@ -45,18 +45,14 @@ def create_paper_figures_table(self): cur.execute(""" CREATE TABLE IF NOT EXISTS arxiv_paper_figures ( paper_arxiv_id VARCHAR(100) NOT NULL, - figure_id INTEGER NOT NULL + figure_id INTEGER NOT NULL, + CONSTRAINT ux_arxiv_paper_figures_unique UNIQUE (paper_arxiv_id, figure_id) ) """) - # Composite unique index for conflict prevention - cur.execute(""" - CREATE UNIQUE INDEX IF NOT EXISTS ux_arxiv_paper_figures_unique - ON arxiv_paper_figures (paper_arxiv_id, figure_id) - """) cur.close() finally: - conn.close() - + conn.close() + # ------------------------- # Insert # ------------------------- diff --git a/research_arcade/sql_database/sql_arxiv_paper_tables.py b/research_arcade/sql_database/sql_arxiv_paper_tables.py index 7eb0f3a..ba25c18 100644 --- a/research_arcade/sql_database/sql_arxiv_paper_tables.py +++ b/research_arcade/sql_database/sql_arxiv_paper_tables.py @@ -7,6 +7,7 @@ from ..arxiv_utils.multi_input.multi_download import MultiDownload from ..arxiv_utils.graph_constructor.node_processor import NodeConstructor from ..arxiv_utils.utils import arxiv_id_processor +import pandas as pd class SQLArxivPaperTable: @@ -40,18 +41,15 @@ def create_paper_tables_table(self): cur.execute(""" CREATE TABLE IF NOT EXISTS arxiv_paper_tables ( paper_arxiv_id VARCHAR(100) NOT NULL, - table_id INTEGER NOT NULL + table_id INTEGER NOT NULL, + CONSTRAINT ux_arxiv_paper_tables_unique UNIQUE (paper_arxiv_id, table_id) ) """) - # Composite unique index mirrors CSV conflict check - cur.execute(""" - CREATE UNIQUE INDEX IF NOT EXISTS ux_arxiv_paper_tables_unique - ON arxiv_paper_tables (paper_arxiv_id, table_id) - """) cur.close() finally: conn.close() + def insert_paper_table(self, paper_arxiv_id, table_id): """ Insert a (paper_arxiv_id, table_id) edge. @@ -178,6 +176,70 @@ def delete_paper_table_by_table_id(self, table_id: int) -> int: finally: conn.close() + + def construct_table_from_csv(self, csv_file): + """ + Construct the paper-table relationships from an external CSV file. + + Args: + csv_file: Path to the CSV file + + Expected CSV format: + - Required columns: paper_arxiv_id, table_id + + Returns: + bool: True if successful, False otherwise + """ + if not os.path.exists(csv_file): + print(f"Error: CSV file {csv_file} does not exist.") + return False + + try: + + # Read the CSV file + df = pd.read_csv(csv_file) + + # Check for required columns + required_cols = ['paper_arxiv_id', 'table_id'] + missing_cols = [col for col in required_cols if col not in df.columns] + + if missing_cols: + print(f"Error: External CSV is missing required columns: {missing_cols}") + return False + + # Convert to list of tuples for bulk insert + rows = list(df[required_cols].itertuples(index=False, name=None)) + + if not rows: + print("No rows to import.") + return True + + # Bulk insert with conflict handling + conn = self._get_connection() + try: + cur = conn.cursor() + psycopg2.extras.execute_values( + cur, + """ + INSERT INTO arxiv_paper_tables (paper_arxiv_id, table_id) + VALUES %s + ON CONFLICT ON CONSTRAINT ux_arxiv_paper_tables_unique DO NOTHING + """, + rows, + page_size=1000 + ) + cur.close() + finally: + conn.close() + + print(f"Successfully imported {len(rows)} paper-table relationships from {csv_file}") + return True + + except Exception as e: + print(f"Error importing paper-table relationships from CSV: {e}") + return False + + def construct_table_from_json(self, json_file): """ Construct the paper-table relationships from an external JSON file. diff --git a/research_arcade/sql_database/sql_arxiv_paragraph_references.py b/research_arcade/sql_database/sql_arxiv_paragraph_references.py index 7ad084b..f99d270 100644 --- a/research_arcade/sql_database/sql_arxiv_paragraph_references.py +++ b/research_arcade/sql_database/sql_arxiv_paragraph_references.py @@ -46,7 +46,7 @@ def create_paragraph_references_table(self): cur.execute(""" CREATE TABLE IF NOT EXISTS arxiv_paragraph_references ( id SERIAL PRIMARY KEY, - paragraph_id VARCHAR(255) NOT NULL, + paragraph_id INT NOT NULL, paper_section VARCHAR(255) NOT NULL, paper_arxiv_id VARCHAR(100) NOT NULL, reference_label VARCHAR(255), diff --git a/research_arcade/sql_database/sql_arxiv_sections.py b/research_arcade/sql_database/sql_arxiv_sections.py index 5c9192d..d79d5f9 100644 --- a/research_arcade/sql_database/sql_arxiv_sections.py +++ b/research_arcade/sql_database/sql_arxiv_sections.py @@ -68,7 +68,7 @@ def insert_section(self, content, title, appendix, paper_arxiv_id, section_in_pa VALUES (%s, %s, %s, %s) RETURNING id """, - (content, title, is_appendix, paper_arxiv_id) + (content, title, appendix, paper_arxiv_id) ) res = cur.fetchone() cur.close() diff --git a/research_arcade_complete_tutorial_with_imports.ipynb b/research_arcade_complete_tutorial_with_imports.ipynb index 8749487..6677eb5 100644 --- a/research_arcade_complete_tutorial_with_imports.ipynb +++ b/research_arcade_complete_tutorial_with_imports.ipynb @@ -66,7 +66,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "169f7a6d", "metadata": {}, "outputs": [], @@ -89,7 +89,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "fa5a88db", "metadata": {}, "outputs": [], @@ -134,7 +134,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "ccaeefb3", "metadata": {}, "outputs": [ @@ -160,7 +160,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -185,7 +185,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -211,7 +211,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "insert-paper", "metadata": {}, "outputs": [ @@ -241,7 +241,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "id": "insert-paper-bert", "metadata": {}, "outputs": [ @@ -279,7 +279,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "id": "get-all-papers", "metadata": {}, "outputs": [ @@ -290,7 +290,7 @@ "Total papers in database: 3\n", "\n", "First 5 papers:\n", - "[(2, '1903.03894v4', '1903.03894', '4', 'GNNExplainer: Generating Explanations for Graph Neural Networks', \"Graph Neural Networks (GNNs) are a powerful tool for machine learning on\\ngraphs.GNNs combine node feature information with the graph structure by\\nrecursively passing neural messages along edges of the input graph. However,\\nincorporating both graph structure and feature information leads to complex\\nmodels, and explaining predictions made by GNNs remains unsolved. Here we\\npropose GNNExplainer, the first general, model-agnostic approach for providing\\ninterpretable explanations for predictions of any GNN-based model on any\\ngraph-based machine learning task. Given an instance, GNNExplainer identifies a\\ncompact subgraph structure and a small subset of node features that have a\\ncrucial role in GNN's prediction. Further, GNNExplainer can generate consistent\\nand concise explanations for an entire class of instances. We formulate\\nGNNExplainer as an optimization task that maximizes the mutual information\\nbetween a GNN's prediction and distribution of possible subgraph structures.\\nExperiments on synthetic and real-world graphs show that our approach can\\nidentify important graph structures as well as node features, and outperforms\\nbaselines by 17.1% on average. GNNExplainer provides a variety of benefits,\\nfrom the ability to visualize semantically relevant structures to\\ninterpretability, to giving insights into errors of faulty GNNs.\", datetime.datetime(2019, 3, 10, 0, 56, 26), {'id': '1903.03894v4', 'url': 'http://arxiv.org/abs/1903.03894v4', 'title': 'GNNExplainer: Generating Explanations for Graph Neural Networks', 'authors': ['Rex Ying', 'Dylan Bourgeois', 'Jiaxuan You', 'Marinka Zitnik', 'Jure Leskovec'], 'abstract': \"Graph Neural Networks (GNNs) are a powerful tool for machine learning on\\ngraphs.GNNs combine node feature information with the graph structure by\\nrecursively passing neural messages along edges of the input graph. However,\\nincorporating both graph structure and feature information leads to complex\\nmodels, and explaining predictions made by GNNs remains unsolved. Here we\\npropose GNNExplainer, the first general, model-agnostic approach for providing\\ninterpretable explanations for predictions of any GNN-based model on any\\ngraph-based machine learning task. Given an instance, GNNExplainer identifies a\\ncompact subgraph structure and a small subset of node features that have a\\ncrucial role in GNN's prediction. Further, GNNExplainer can generate consistent\\nand concise explanations for an entire class of instances. We formulate\\nGNNExplainer as an optimization task that maximizes the mutual information\\nbetween a GNN's prediction and distribution of possible subgraph structures.\\nExperiments on synthetic and real-world graphs show that our approach can\\nidentify important graph structures as well as node features, and outperforms\\nbaselines by 17.1% on average. GNNExplainer provides a variety of benefits,\\nfrom the ability to visualize semantically relevant structures to\\ninterpretability, to giving insights into errors of faulty GNNs.\", 'published': '2019-03-10 00:56:26+00:00', 'categories': ['cs.LG', 'stat.ML']}), (5, '1706.03762v7', '1706.03762', '7', 'Attention Is All You Need', 'The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.', datetime.datetime(2017, 6, 12, 0, 0), {'venue': 'NeurIPS 2017', 'pdf_url': 'https://arxiv.org/pdf/1706.03762.pdf'}), (6, '1810.04805v2', '1810.04805', '2', 'BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding', 'We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications.', datetime.datetime(2018, 10, 11, 0, 0), {'venue': 'NAACL 2019', 'citations': 50000})]\n" + "[(2, '1903.03894v4', '1903.03894', '4', 'GNNExplainer: Generating Explanations for Graph Neural Networks', \"Graph Neural Networks (GNNs) are a powerful tool for machine learning on\\ngraphs.GNNs combine node feature information with the graph structure by\\nrecursively passing neural messages along edges of the input graph. However,\\nincorporating both graph structure and feature information leads to complex\\nmodels, and explaining predictions made by GNNs remains unsolved. Here we\\npropose GNNExplainer, the first general, model-agnostic approach for providing\\ninterpretable explanations for predictions of any GNN-based model on any\\ngraph-based machine learning task. Given an instance, GNNExplainer identifies a\\ncompact subgraph structure and a small subset of node features that have a\\ncrucial role in GNN's prediction. Further, GNNExplainer can generate consistent\\nand concise explanations for an entire class of instances. We formulate\\nGNNExplainer as an optimization task that maximizes the mutual information\\nbetween a GNN's prediction and distribution of possible subgraph structures.\\nExperiments on synthetic and real-world graphs show that our approach can\\nidentify important graph structures as well as node features, and outperforms\\nbaselines by 17.1% on average. GNNExplainer provides a variety of benefits,\\nfrom the ability to visualize semantically relevant structures to\\ninterpretability, to giving insights into errors of faulty GNNs.\", datetime.datetime(2019, 3, 10, 0, 56, 26), {'id': '1903.03894v4', 'url': 'http://arxiv.org/abs/1903.03894v4', 'title': 'GNNExplainer: Generating Explanations for Graph Neural Networks', 'authors': ['Rex Ying', 'Dylan Bourgeois', 'Jiaxuan You', 'Marinka Zitnik', 'Jure Leskovec'], 'abstract': \"Graph Neural Networks (GNNs) are a powerful tool for machine learning on\\ngraphs.GNNs combine node feature information with the graph structure by\\nrecursively passing neural messages along edges of the input graph. However,\\nincorporating both graph structure and feature information leads to complex\\nmodels, and explaining predictions made by GNNs remains unsolved. Here we\\npropose GNNExplainer, the first general, model-agnostic approach for providing\\ninterpretable explanations for predictions of any GNN-based model on any\\ngraph-based machine learning task. Given an instance, GNNExplainer identifies a\\ncompact subgraph structure and a small subset of node features that have a\\ncrucial role in GNN's prediction. Further, GNNExplainer can generate consistent\\nand concise explanations for an entire class of instances. We formulate\\nGNNExplainer as an optimization task that maximizes the mutual information\\nbetween a GNN's prediction and distribution of possible subgraph structures.\\nExperiments on synthetic and real-world graphs show that our approach can\\nidentify important graph structures as well as node features, and outperforms\\nbaselines by 17.1% on average. GNNExplainer provides a variety of benefits,\\nfrom the ability to visualize semantically relevant structures to\\ninterpretability, to giving insights into errors of faulty GNNs.\", 'published': '2019-03-10 00:56:26+00:00', 'categories': ['cs.LG', 'stat.ML']}), (3, '1706.03762v7', '1706.03762', '7', 'Attention Is All You Need', 'The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.', datetime.datetime(2017, 6, 12, 0, 0), {'venue': 'NeurIPS 2017', 'pdf_url': 'https://arxiv.org/pdf/1706.03762.pdf'}), (4, '1810.04805v2', '1810.04805', '2', 'BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding', 'We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications.', datetime.datetime(2018, 10, 11, 0, 0), {'venue': 'NAACL 2019', 'citations': 50000})]\n" ] } ], @@ -311,20 +311,16 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "id": "get-paper-by-id", "metadata": {}, "outputs": [ { - "ename": "AttributeError", - "evalue": "'SQLArxivPapers' object has no attribute 'get_paper_by_id'", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mAttributeError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[9]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m 1\u001b[39m paper_id = {\u001b[33m\"\u001b[39m\u001b[33marxiv_id\u001b[39m\u001b[33m\"\u001b[39m: \u001b[33m\"\u001b[39m\u001b[33m1810.04805v2\u001b[39m\u001b[33m\"\u001b[39m}\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m paper_features = \u001b[43mresearch_arcade\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget_node_features_by_id\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43marxiv_papers\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpaper_id\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 3\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33mPaper details:\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 4\u001b[39m \u001b[38;5;28mprint\u001b[39m(paper_features.to_dict(orient=\u001b[33m\"\u001b[39m\u001b[33mrecords\u001b[39m\u001b[33m\"\u001b[39m)[\u001b[32m0\u001b[39m])\n", - "\u001b[36mFile \u001b[39m\u001b[32m/data/cl195/paper-crawler/research_arcade/research_arcade.py:224\u001b[39m, in \u001b[36mResearchArcade.get_node_features_by_id\u001b[39m\u001b[34m(self, table, primary_key)\u001b[39m\n\u001b[32m 222\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.arxiv_tables.get_table_by_id(**primary_key)\n\u001b[32m 223\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m table == \u001b[33m'\u001b[39m\u001b[33marxiv_papers\u001b[39m\u001b[33m'\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m224\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43marxiv_papers\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget_paper_by_id\u001b[49m(**primary_key)\n\u001b[32m 225\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m table == \u001b[33m'\u001b[39m\u001b[33marxiv_paragraphs\u001b[39m\u001b[33m'\u001b[39m:\n\u001b[32m 226\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.arxiv_paragraphs.get_paragraph_by_id(**primary_key)\n", - "\u001b[31mAttributeError\u001b[39m: 'SQLArxivPapers' object has no attribute 'get_paper_by_id'" + "name": "stdout", + "output_type": "stream", + "text": [ + "Paper details:\n", + "(4, '1810.04805v2', '1810.04805', '2', 'BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding', 'We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications.', datetime.datetime(2018, 10, 11, 0, 0), {'venue': 'NAACL 2019', 'citations': 50000})\n" ] } ], @@ -332,7 +328,7 @@ "paper_id = {\"arxiv_id\": \"1810.04805v2\"}\n", "paper_features = research_arcade.get_node_features_by_id(\"arxiv_papers\", paper_id)\n", "print(\"Paper details:\")\n", - "print(paper_features.to_dict(orient=\"records\")[0])" + "print(paper_features)" ] }, { @@ -345,7 +341,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "id": "update-paper", "metadata": {}, "outputs": [ @@ -383,20 +379,16 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "id": "delete-paper", "metadata": {}, "outputs": [ { - "ename": "AttributeError", - "evalue": "'SQLArxivPapers' object has no attribute 'delete_paper_by_id'", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mAttributeError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[11]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;66;03m# Delete a paper by ID\u001b[39;00m\n\u001b[32m 2\u001b[39m paper_id = {\u001b[33m\"\u001b[39m\u001b[33marxiv_id\u001b[39m\u001b[33m\"\u001b[39m: \u001b[33m\"\u001b[39m\u001b[33m1706.03762v7\u001b[39m\u001b[33m\"\u001b[39m}\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m deleted_paper = \u001b[43mresearch_arcade\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdelete_node_by_id\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43marxiv_papers\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpaper_id\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 4\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33mDeleted paper:\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 5\u001b[39m \u001b[38;5;28mprint\u001b[39m(deleted_paper)\n", - "\u001b[36mFile \u001b[39m\u001b[32m/data/cl195/paper-crawler/research_arcade/research_arcade.py:163\u001b[39m, in \u001b[36mResearchArcade.delete_node_by_id\u001b[39m\u001b[34m(self, table, primary_key)\u001b[39m\n\u001b[32m 161\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.arxiv_tables.delete_table_by_id(**primary_key)\n\u001b[32m 162\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m table == \u001b[33m'\u001b[39m\u001b[33marxiv_papers\u001b[39m\u001b[33m'\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m163\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43marxiv_papers\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdelete_paper_by_id\u001b[49m(**primary_key)\n\u001b[32m 164\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m table == \u001b[33m'\u001b[39m\u001b[33marxiv_paragraphs\u001b[39m\u001b[33m'\u001b[39m:\n\u001b[32m 165\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.arxiv_paragraphs.delete_paragraph_by_id(**primary_key)\n", - "\u001b[31mAttributeError\u001b[39m: 'SQLArxivPapers' object has no attribute 'delete_paper_by_id'" + "name": "stdout", + "output_type": "stream", + "text": [ + "Deleted paper:\n", + "True\n" ] } ], @@ -432,7 +424,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "c18c7737", "metadata": {}, "outputs": [], @@ -450,7 +442,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -475,7 +467,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -501,7 +493,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 16, "id": "insert-authors", "metadata": {}, "outputs": [ @@ -562,7 +554,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 17, "id": "get-all-authors", "metadata": {}, "outputs": [ @@ -570,10 +562,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "Total authors in database: 14391\n", + "Total authors in database: 5\n", "\n", "All authors:\n", - "[(1, '2288033664', 'Zhe Xu', 'https://www.semanticscholar.org/author/2288033664'), (2, '49025612', 'Daoyuan Chen', 'https://www.semanticscholar.org/author/49025612'), (3, '2283309816', 'Zhenqing Ling', 'https://www.semanticscholar.org/author/2283309816'), (4, '2237607166', 'Yaliang Li', 'https://www.semanticscholar.org/author/2237607166'), (5, '2288065597', 'Ying Shen', 'https://www.semanticscholar.org/author/2288065597'), (6, '2249533760', 'Ziyu Wan', 'https://www.semanticscholar.org/author/2249533760'), (7, '2296444976', 'Yunxiang Li', 'https://www.semanticscholar.org/author/2296444976'), (8, '2326075771', 'Yan Song', 'https://www.semanticscholar.org/author/2326075771'), (9, '2345961411', 'Hanjing Wang', 'https://www.semanticscholar.org/author/2345961411'), (10, '2284931437', 'Linyi Yang', 'https://www.semanticscholar.org/author/2284931437'), (11, '2351406620', 'Mark Schmidt', 'https://www.semanticscholar.org/author/2351406620'), (12, '2346345701', 'Jun Wang', 'https://www.semanticscholar.org/author/2346345701'), (13, '2244690305', 'Weinan Zhang', 'https://www.semanticscholar.org/author/2244690305'), (14, '2349689215', 'Shuyue Hu', 'https://www.semanticscholar.org/author/2349689215'), (15, '2283887572', 'Ying Wen', 'https://www.semanticscholar.org/author/2283887572'), (31, '2352065447', 'Bin Hu', 'https://www.semanticscholar.org/author/2352065447'), (32, '2345800729', 'Chen-Xiao Gao', 'https://www.semanticscholar.org/author/2345800729'), (33, '2349742424', 'Shurui Liu', 'https://www.semanticscholar.org/author/2349742424'), (34, '2282499981', 'Junjie Guo', 'https://www.semanticscholar.org/author/2282499981'), (35, '2319685693', 'Fang Chen', 'https://www.semanticscholar.org/author/2319685693'), (36, '2281607927', 'Fangcen Liu', 'https://www.semanticscholar.org/author/2281607927'), (37, '2269992285', 'Alexey Kuznetsov', 'https://www.semanticscholar.org/author/2269992285'), (38, '2332536645', 'Zak Buzzard', 'https://www.semanticscholar.org/author/2332536645'), (39, '2349744028', 'Haiyan Wei', 'https://www.semanticscholar.org/author/2349744028'), (40, '2333228335', 'Hangrui Xu', 'https://www.semanticscholar.org/author/2333228335'), (41, '2350761962', 'Bingxu Zhu', 'https://www.semanticscholar.org/author/2350761962'), (42, '2349642215', 'Yulian Geng', 'https://www.semanticscholar.org/author/2349642215'), (43, '2349742956', 'Aolei Liu', 'https://www.semanticscholar.org/author/2349742956'), (44, '2348482285', 'Wenfei Yin', 'https://www.semanticscholar.org/author/2348482285'), (45, '2349355763', 'Jian Liu', 'https://www.semanticscholar.org/author/2349355763'), (46, '2350511814', 'Leo Zanotti', 'https://www.semanticscholar.org/author/2350511814'), (47, '121713030', 'Yuxu Lu', 'https://www.semanticscholar.org/author/121713030'), (48, '2346897723', 'Ai Chen', 'https://www.semanticscholar.org/author/2346897723'), (49, '2274210597', 'Dong Yang', 'https://www.semanticscholar.org/author/2274210597'), (50, '2268299485', 'Ryan Wen Liu', 'https://www.semanticscholar.org/author/2268299485'), (51, '2344835059', 'Gabriela Ozegovic', 'https://www.semanticscholar.org/author/2344835059'), (52, '3392184', 'Thorsten Ruprechter', 'https://www.semanticscholar.org/author/3392184'), (53, '2260340612', 'Denis Helic', 'https://www.semanticscholar.org/author/2260340612'), (54, '2356553292', 'Saif Bashar', 'https://www.semanticscholar.org/author/2356553292'), (55, '2356548090', 'Samia Nasir Nira', 'https://www.semanticscholar.org/author/2356548090'), (56, '2148672772', 'Shabbir Mahmood', 'https://www.semanticscholar.org/author/2148672772'), (57, '2213071', 'Humaun Kabir', 'https://www.semanticscholar.org/author/2213071'), (58, '2130904146', 'Sujit Roy', 'https://www.semanticscholar.org/author/2130904146'), (59, '51068913', 'Iffat Farhana', 'https://www.semanticscholar.org/author/51068913'), (60, '2343862566', 'Yunha Yeo', 'https://www.semanticscholar.org/author/2343862566'), (61, '2343918326', 'Daeho Um', 'https://www.semanticscholar.org/author/2343918326'), (62, '2354755099', 'Zhengxi Lu', 'https://www.semanticscholar.org/author/2354755099'), (63, '2297996414', 'Shizhuo Cheng', 'https://www.semanticscholar.org/author/2297996414'), (64, '2361482549', 'Tintin Jiang', 'https://www.semanticscholar.org/author/2361482549'), (65, '2256617112', 'Yan Zhang', 'https://www.semanticscholar.org/author/2256617112'), (66, '2337830419', 'Min Zhang', 'https://www.semanticscholar.org/author/2337830419'), (67, '2349304855', 'Yanshu Li', 'https://www.semanticscholar.org/author/2349304855'), (68, '2349645189', 'Raquel Ana Magalhaes Bush', 'https://www.semanticscholar.org/author/2349645189'), (69, '2363350631', 'Tian Yun', 'https://www.semanticscholar.org/author/2363350631'), (70, '2363370784', 'Jianjiang Yang', 'https://www.semanticscholar.org/author/2363370784'), (71, '2363349096', 'Pinyuan Feng', 'https://www.semanticscholar.org/author/2363349096'), (72, '2321772748', 'Peng Chen', 'https://www.semanticscholar.org/author/2321772748'), (73, '2363533979', 'Jinfa Huang', 'https://www.semanticscholar.org/author/2363533979'), (74, '2321871796', 'Pi Bu', 'https://www.semanticscholar.org/author/2321871796'), (75, '2355036537', 'Ruixiang Tang', 'https://www.semanticscholar.org/author/2355036537'), (76, '2336243225', 'Yingyao Wang', 'https://www.semanticscholar.org/author/2336243225'), (77, '2349678076', 'Xinyi Wang', 'https://www.semanticscholar.org/author/2349678076'), (78, '2303300696', 'Ziming Wang', 'https://www.semanticscholar.org/author/2303300696'), (79, '2349740196', 'Jie Guo', 'https://www.semanticscholar.org/author/2349740196'), (80, '2345919965', 'Yingxiu Zhao', 'https://www.semanticscholar.org/author/2345919965'), (81, '2349749055', 'Qi Zhu', 'https://www.semanticscholar.org/author/2349749055'), (82, '2303303180', 'Jun Song', 'https://www.semanticscholar.org/author/2303303180'), (83, '2305566956', 'Siran Yang', 'https://www.semanticscholar.org/author/2305566956'), (84, '2267489132', 'Tyler Han', 'https://www.semanticscholar.org/author/2267489132'), (85, '2303721288', 'Preet Shah', 'https://www.semanticscholar.org/author/2303721288'), (86, '2344846911', 'Sidharth Rajagopal', 'https://www.semanticscholar.org/author/2344846911'), (87, '2345693084', 'Yanda Bao', 'https://www.semanticscholar.org/author/2345693084'), (88, '2344968528', 'Sanghun Jung', 'https://www.semanticscholar.org/author/2344968528'), (89, '1491643710', 'Sidharth Talia', 'https://www.semanticscholar.org/author/1491643710'), (90, '2344834583', 'Gabriel Guo', 'https://www.semanticscholar.org/author/2344834583'), (91, '2344890191', 'Bryan Xu', 'https://www.semanticscholar.org/author/2344890191'), (92, '2344832766', 'Bhaumik Mehta', 'https://www.semanticscholar.org/author/2344832766'), (93, '2344831174', 'Emma Romig', 'https://www.semanticscholar.org/author/2344831174'), (94, '2315511167', 'Rosario Scalise', 'https://www.semanticscholar.org/author/2315511167'), (95, '2292033603', 'Byron Boots', 'https://www.semanticscholar.org/author/2292033603'), (96, '2109052643', 'Xiaozhao Liu', 'https://www.semanticscholar.org/author/2109052643'), (97, '2363350832', 'Dinggang Shen', 'https://www.semanticscholar.org/author/2363350832'), (98, '2363401323', 'Xihui Liu', 'https://www.semanticscholar.org/author/2363401323'), (99, '2109013955', 'Jiamang Wang', 'https://www.semanticscholar.org/author/2109013955'), (100, '2152215683', 'Bo Zheng', 'https://www.semanticscholar.org/author/2152215683'), (101, '2344822327', 'Haoyan Yang', 'https://www.semanticscholar.org/author/2344822327'), (102, '2349962830', 'Pan Xu', 'https://www.semanticscholar.org/author/2349962830'), (103, '1491239652', 'Runxue Bao', 'https://www.semanticscholar.org/author/1491239652'), (104, '2326815721', 'Cao Xiao', 'https://www.semanticscholar.org/author/2326815721'), (105, '2363453012', 'Jun Ma', 'https://www.semanticscholar.org/author/2363453012'), (106, '2303385525', 'Parminder Bhatia', 'https://www.semanticscholar.org/author/2303385525'), (107, '2322616804', 'Shangqian Gao', 'https://www.semanticscholar.org/author/2322616804'), (108, '1405125430', 'Taha A. Kass-Hout', 'https://www.semanticscholar.org/author/1405125430'), (109, '2240081574', 'Hongyu An', 'https://www.semanticscholar.org/author/2240081574'), (110, '2350765490', 'Chengyu Wang', 'https://www.semanticscholar.org/author/2350765490'), (111, '2302609686', 'Xinfeng Zhang', 'https://www.semanticscholar.org/author/2302609686'), (112, '2110772934', 'Shijie Zhao', 'https://www.semanticscholar.org/author/2110772934'), (113, '2243406637', 'Junbing Yan', 'https://www.semanticscholar.org/author/2243406637'), (114, '2356378210', 'Wenrui Cai', 'https://www.semanticscholar.org/author/2356378210'), (115, '2326792244', 'Li Zhang', 'https://www.semanticscholar.org/author/2326792244'), (116, '2302785323', 'Yuanhao Yue', 'https://www.semanticscholar.org/author/2302785323'), (117, '2267221385', 'Jun Huang', 'https://www.semanticscholar.org/author/2267221385'), (118, '2261851929', 'Boxiong Wang', 'https://www.semanticscholar.org/author/2261851929'), (119, '2290026565', 'Hui Kang', 'https://www.semanticscholar.org/author/2290026565'), (120, '2189016616', 'Leizhen Wang', 'https://www.semanticscholar.org/author/2189016616'), (121, '2108959135', 'Jiahui Li', 'https://www.semanticscholar.org/author/2108959135'), (122, '2237519002', 'Peibo Duan', 'https://www.semanticscholar.org/author/2237519002'), (123, '2333603760', 'Cheng Lyu', 'https://www.semanticscholar.org/author/2333603760'), (124, '2314115348', 'Adam Karvonen', 'https://www.semanticscholar.org/author/2314115348'), (125, '2257034392', 'Can Rager', 'https://www.semanticscholar.org/author/2257034392'), (126, '2349741236', 'Johnny Lin', 'https://www.semanticscholar.org/author/2349741236'), (127, '2218145401', 'Curt Tigges', 'https://www.semanticscholar.org/author/2218145401'), (128, '2308099558', 'Joseph Bloom', 'https://www.semanticscholar.org/author/2308099558'), (129, '2311692851', 'David Chanin', 'https://www.semanticscholar.org/author/2311692851'), (130, '2258718745', 'Yeu-Tong Lau', 'https://www.semanticscholar.org/author/2258718745'), (131, '2308039511', 'Santiago Acevedo', 'https://www.semanticscholar.org/author/2308039511'), (132, '2363346431', 'Andrea Mascaretti', 'https://www.semanticscholar.org/author/2363346431'), (133, '2190427709', 'Riccardo Rende', 'https://www.semanticscholar.org/author/2190427709'), (134, '1581336361', \"Mat'eo Mahaut\", 'https://www.semanticscholar.org/author/1581336361'), (135, '2096105428', 'Lenka Ptackova', 'https://www.semanticscholar.org/author/2096105428'), (136, '2332019572', 'Antony Seabra', 'https://www.semanticscholar.org/author/2332019572'), (137, '2332022387', 'Sérgio Lifschitz', 'https://www.semanticscholar.org/author/2332022387'), (138, '2287266864', 'Geng Sun', 'https://www.semanticscholar.org/author/2287266864'), (139, '2334011827', 'Zhenliang Ma', 'https://www.semanticscholar.org/author/2334011827'), (140, '29769448', 'Zemin Sun', 'https://www.semanticscholar.org/author/29769448'), (141, '2231660058', 'Ze Zhao', 'https://www.semanticscholar.org/author/2231660058'), (142, '2330569282', 'Jiacheng Wang', 'https://www.semanticscholar.org/author/2330569282'), (143, '2064914349', 'Bin Lu', 'https://www.semanticscholar.org/author/2064914349'), (144, '2363812441', 'Yoojin Kwon', 'https://www.semanticscholar.org/author/2363812441'), (145, '2283029302', 'Xiaoying Gan', 'https://www.semanticscholar.org/author/2283029302'), (146, '2363574509', 'Hongjun Suh', 'https://www.semanticscholar.org/author/2363574509'), (147, '2363673767', 'Wooseok Lee', 'https://www.semanticscholar.org/author/2363673767'), (148, '2310820436', 'Gu Tang', 'https://www.semanticscholar.org/author/2310820436'), (149, '2348424160', 'Taesik Gong', 'https://www.semanticscholar.org/author/2348424160'), (150, '1713586', 'D. 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Susanto', 'https://www.semanticscholar.org/author/2244760572'), (14831, '2326830121', 'Zhonglin Cao', 'https://www.semanticscholar.org/author/2326830121'), (14832, '2253522157', 'Mario Geiger', 'https://www.semanticscholar.org/author/2253522157'), (14833, '2256147064', 'Allan dos Santos Costa', 'https://www.semanticscholar.org/author/2256147064'), (14834, '2220633442', 'Danny Reidenbach', 'https://www.semanticscholar.org/author/2220633442'), (14835, '2309248898', 'Karsten Kreis', 'https://www.semanticscholar.org/author/2309248898'), (14836, '17966996', 'Tomas Geffner', 'https://www.semanticscholar.org/author/17966996'), (14837, '2325908889', 'Franco Pellegrini', 'https://www.semanticscholar.org/author/2325908889'), (14838, '2312688016', 'Guoqing Zhou', 'https://www.semanticscholar.org/author/2312688016'), (14839, '2354227094', 'Emine Küçükbenli', 'https://www.semanticscholar.org/author/2354227094'), (14840, '2373430803', 'Paulo Salem', 'https://www.semanticscholar.org/author/2373430803'), (14841, '2373437823', 'Robert Sim', 'https://www.semanticscholar.org/author/2373437823'), (14917, '2348306168', 'Yi Wu', 'https://www.semanticscholar.org/author/2348306168'), (14842, '2372676124', 'Christopher Olsen', 'https://www.semanticscholar.org/author/2372676124'), (14843, '2372658537', 'Prerit Saxena', 'https://www.semanticscholar.org/author/2372658537'), (14844, '2373432201', 'Rafael Barcelos', 'https://www.semanticscholar.org/author/2373432201'), (14845, '2372541672', 'Yi Ding', 'https://www.semanticscholar.org/author/2372541672'), (14846, '2273810162', 'Prashant Govindarajan', 'https://www.semanticscholar.org/author/2273810162'), (14847, '2373280285', 'Davide Baldelli', 'https://www.semanticscholar.org/author/2373280285'), (14848, '2372738362', 'Jay Pathak', 'https://www.semanticscholar.org/author/2372738362'), (14849, '2303408438', 'Quentin Fournier', 'https://www.semanticscholar.org/author/2303408438'), (14850, '123607932', 'Sarath Chandar', 'https://www.semanticscholar.org/author/123607932'), (14851, '2191632885', 'Minjae Jeon', 'https://www.semanticscholar.org/author/2191632885'), (14852, '2069440378', 'Lang Tong', 'https://www.semanticscholar.org/author/2069440378'), (14853, '2191650766', 'Qing Zhao', 'https://www.semanticscholar.org/author/2191650766'), (14854, '2284734029', 'Ping Liu', 'https://www.semanticscholar.org/author/2284734029'), (14855, '2373397273', 'Rajat Arora', 'https://www.semanticscholar.org/author/2373397273'), (14856, '2284822947', 'Xiao Shi', 'https://www.semanticscholar.org/author/2284822947'), (14857, '2372425492', 'Benjamin Le', 'https://www.semanticscholar.org/author/2372425492'), (14858, '2283843396', 'Qianqi Shen', 'https://www.semanticscholar.org/author/2283843396'), (14859, '2285053333', 'Jianqiang Shen', 'https://www.semanticscholar.org/author/2285053333'), (14860, '2281105063', 'Chen-Chen Jiang', 'https://www.semanticscholar.org/author/2281105063'), (14861, '2373387591', 'Nikita Zhiltsov', 'https://www.semanticscholar.org/author/2373387591'), (14863, '2373555800', 'Yidan Zhu', 'https://www.semanticscholar.org/author/2373555800'), (14864, '2374420968', 'Liming Dong', 'https://www.semanticscholar.org/author/2374420968'), (14865, '2175570239', 'Haichao Wei', 'https://www.semanticscholar.org/author/2175570239'), (14866, '2306767750', 'Qinglei Guo', 'https://www.semanticscholar.org/author/2306767750'), (14867, '2284861972', 'Luke Simon', 'https://www.semanticscholar.org/author/2284861972'), (14868, '2264620213', 'Liangjie Hong', 'https://www.semanticscholar.org/author/2264620213'), (14869, '2276750410', 'Wenjing Zhang', 'https://www.semanticscholar.org/author/2276750410'), (14870, '2120333451', 'Aashish Gottipati', 'https://www.semanticscholar.org/author/2120333451'), (14871, '2259937079', 'Lili Qiu', 'https://www.semanticscholar.org/author/2259937079'), (14872, '2220363257', 'Audri Banik', 'https://www.semanticscholar.org/author/2220363257'), (14873, '2372451285', 'Glaucio Haroldo Silva de Carvalho', 'https://www.semanticscholar.org/author/2372451285'), (14874, '2332050850', 'Renata Dividino', 'https://www.semanticscholar.org/author/2332050850'), (14875, '2373329059', 'Peter Barnett', 'https://www.semanticscholar.org/author/2373329059'), (14876, '2310394463', 'Younggun Kim', 'https://www.semanticscholar.org/author/2310394463'), (14877, '2316558060', 'Ahmed S. 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Tariq', 'https://www.semanticscholar.org/author/2135385816'), (14890, '2244624975', 'Sangjae Bae', 'https://www.semanticscholar.org/author/2244624975'), (14891, '3431333', 'David Isele', 'https://www.semanticscholar.org/author/3431333'), (14892, '2273556830', 'Fabio Pasqualetti', 'https://www.semanticscholar.org/author/2273556830'), (14893, '2211340412', \"Jovin D'sa\", 'https://www.semanticscholar.org/author/2211340412'), (14894, '2111939263', 'Ekaterina Borodich', 'https://www.semanticscholar.org/author/2111939263'), (14895, '2331856454', 'Dmitry Kovalev', 'https://www.semanticscholar.org/author/2331856454'), (14896, '66166455', 'Shion Takeno', 'https://www.semanticscholar.org/author/66166455'), (14897, '84107750', 'Yu Inatsu', 'https://www.semanticscholar.org/author/84107750'), (14898, '2314377120', 'Masayuki Karasuyama', 'https://www.semanticscholar.org/author/2314377120'), (14899, '2154095046', 'Ichiro Takeuchi', 'https://www.semanticscholar.org/author/2154095046'), (14900, 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'Cheng Long', 'https://www.semanticscholar.org/author/2265489619'), (14948, '2365056440', 'Yan Zhao', 'https://www.semanticscholar.org/author/2365056440'), (14949, '2262543561', 'Ziyue Li', 'https://www.semanticscholar.org/author/2262543561'), (14950, '2285316331', 'Panos Kalnis', 'https://www.semanticscholar.org/author/2285316331'), (14951, '2366612994', 'Adhwaa Alchaab', 'https://www.semanticscholar.org/author/2366612994'), (14952, '40449788', 'Ayman Younis', 'https://www.semanticscholar.org/author/40449788'), (14953, '2241828177', 'D. 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S. Khodaei', 'https://www.semanticscholar.org/author/21264055'), (14968, '153622034', 'M. H. Aliabadi', 'https://www.semanticscholar.org/author/153622034'), (14969, '2321664693', 'Xiaojie Li', 'https://www.semanticscholar.org/author/2321664693'), (14970, '2236881361', 'Zhijie Cai', 'https://www.semanticscholar.org/author/2236881361'), (14971, '2372746703', 'Nan Qi', 'https://www.semanticscholar.org/author/2372746703'), (14972, '2376063623', 'Chao Dong', 'https://www.semanticscholar.org/author/2376063623'), (14973, '2346063915', 'Guangxu Zhu', 'https://www.semanticscholar.org/author/2346063915'), (14974, '2374125858', 'Haixia Ma', 'https://www.semanticscholar.org/author/2374125858'), (14976, '2346995791', 'Shi Jin', 'https://www.semanticscholar.org/author/2346995791'), (14977, '2373333793', 'Sebastian Barros Elgueta', 'https://www.semanticscholar.org/author/2373333793'), (14978, '23218541', 'P. M. U. 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Shashua', 'https://www.semanticscholar.org/author/3140335'), (14997, '2373603330', 'Zhaoyang Guan', 'https://www.semanticscholar.org/author/2373603330'), (14998, '2351000034', 'Leyi Zhao', 'https://www.semanticscholar.org/author/2351000034'), (15000, '2375887351', 'Xinyu Ying', 'https://www.semanticscholar.org/author/2375887351'), (15001, '2374241409', 'Hanlin Zhang', 'https://www.semanticscholar.org/author/2374241409'), (15002, '2373605358', 'Michele Pak', 'https://www.semanticscholar.org/author/2373605358'), (15003, '2364955408', 'Yangfan He', 'https://www.semanticscholar.org/author/2364955408'), (15004, '2374195579', 'Yi Xin', 'https://www.semanticscholar.org/author/2374195579'), (15005, '2373748377', 'Jianhui Wang', 'https://www.semanticscholar.org/author/2373748377'), (15007, 'ss_ashish_vaswani', 'Ashish Vaswani', 'https://scholar.google.com/citations?user=oR9sCGYAAAAJ'), (15008, 'ss_noam_shazeer', 'Noam Shazeer', 'https://scholar.google.com/citations?user=oR9sCGYAAAAJ'), (15009, 'ss_niki_parmar', 'Niki Parmar', 'https://scholar.google.com/citations?user=oR9sCGYAAAAJ'), (15010, 'ss_jakob_uszkoreit', 'Jakob Uszkoreit', 'https://scholar.google.com/citations?user=oR9sCGYAAAAJ'), (15011, 'ss_llion_jones', 'Llion Jones', 'https://scholar.google.com/citations?user=oR9sCGYAAAAJ')]\n" + "[(1, 'ss_ashish_vaswani', 'Ashish Vaswani', 'https://scholar.google.com/citations?user=oR9sCGYAAAAJ'), (2, 'ss_noam_shazeer', 'Noam Shazeer', 'https://scholar.google.com/citations?user=oR9sCGYAAAAJ'), (3, 'ss_niki_parmar', 'Niki Parmar', 'https://scholar.google.com/citations?user=oR9sCGYAAAAJ'), (4, 'ss_jakob_uszkoreit', 'Jakob Uszkoreit', 'https://scholar.google.com/citations?user=oR9sCGYAAAAJ'), (5, 'ss_llion_jones', 'Llion Jones', 'https://scholar.google.com/citations?user=oR9sCGYAAAAJ')]\n" ] } ], @@ -594,25 +586,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "get-author-by-id", "metadata": {}, "outputs": [ { - "ename": "TypeError", - "evalue": "SQLArxivAuthors.get_author_by_id() got an unexpected keyword argument 'semantic_scholar_id'", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mTypeError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[20]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m 1\u001b[39m author_id = {\u001b[33m\"\u001b[39m\u001b[33msemantic_scholar_id\u001b[39m\u001b[33m\"\u001b[39m: \u001b[32m12288033664\u001b[39m}\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m author_features = \u001b[43mresearch_arcade\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget_node_features_by_id\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43marxiv_authors\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mauthor_id\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 3\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33mAuthor details:\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 4\u001b[39m \u001b[38;5;28mprint\u001b[39m(author_features)\n", - "\u001b[36mFile \u001b[39m\u001b[32m/data/cl195/paper-crawler/research_arcade/research_arcade.py:216\u001b[39m, in \u001b[36mResearchArcade.get_node_features_by_id\u001b[39m\u001b[34m(self, table, primary_key)\u001b[39m\n\u001b[32m 214\u001b[39m \u001b[38;5;66;03m# Tables in arxiv dataset\u001b[39;00m\n\u001b[32m 215\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m table == \u001b[33m'\u001b[39m\u001b[33marxiv_authors\u001b[39m\u001b[33m'\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m216\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43marxiv_authors\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget_author_by_id\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mprimary_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 217\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m table == \u001b[33m'\u001b[39m\u001b[33marxiv_categories\u001b[39m\u001b[33m'\u001b[39m:\n\u001b[32m 218\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.arxiv_categories.get_category_by_id(**primary_key)\n", - "\u001b[31mTypeError\u001b[39m: SQLArxivAuthors.get_author_by_id() got an unexpected keyword argument 'semantic_scholar_id'" + "name": "stdout", + "output_type": "stream", + "text": [ + "Author details:\n", + "None\n" ] } ], "source": [ - "author_id = {\"semantic_scholar_id\": 2288033664}\n", + "author_id = {\"semantic_scholar_id\": '2288033664'}\n", "author_features = research_arcade.get_node_features_by_id(\"arxiv_authors\", author_id)\n", "print(\"Author details:\")\n", "print(author_features)" @@ -628,20 +616,15 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "id": "update-author", "metadata": {}, "outputs": [ { - "ename": "TypeError", - "evalue": "SQLArxivAuthors.update_author() missing 1 required positional argument: 'id'", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mTypeError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[19]\u001b[39m\u001b[32m, line 6\u001b[39m\n\u001b[32m 1\u001b[39m updated_author = {\n\u001b[32m 2\u001b[39m \u001b[33m'\u001b[39m\u001b[33msemantic_scholar_id\u001b[39m\u001b[33m'\u001b[39m: \u001b[33m'\u001b[39m\u001b[33mss_ashish_vaswani\u001b[39m\u001b[33m'\u001b[39m,\n\u001b[32m 3\u001b[39m \u001b[33m'\u001b[39m\u001b[33mhomepage\u001b[39m\u001b[33m'\u001b[39m: \u001b[33m'\u001b[39m\u001b[33mhttps://ashishvaswani.com\u001b[39m\u001b[33m'\u001b[39m\n\u001b[32m 4\u001b[39m }\n\u001b[32m----> \u001b[39m\u001b[32m6\u001b[39m \u001b[43mresearch_arcade\u001b[49m\u001b[43m.\u001b[49m\u001b[43mupdate_node\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43marxiv_authors\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnode_features\u001b[49m\u001b[43m=\u001b[49m\u001b[43mupdated_author\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 7\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33mAuthor updated successfully!\u001b[39m\u001b[33m\"\u001b[39m)\n", - "\u001b[36mFile \u001b[39m\u001b[32m/data/cl195/paper-crawler/research_arcade/research_arcade.py:185\u001b[39m, in \u001b[36mResearchArcade.update_node\u001b[39m\u001b[34m(self, table, node_features)\u001b[39m\n\u001b[32m 183\u001b[39m \u001b[38;5;66;03m# Tables in arxiv dataset\u001b[39;00m\n\u001b[32m 184\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m table == \u001b[33m'\u001b[39m\u001b[33marxiv_authors\u001b[39m\u001b[33m'\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m185\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43marxiv_authors\u001b[49m\u001b[43m.\u001b[49m\u001b[43mupdate_author\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mnode_features\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 186\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m table == \u001b[33m'\u001b[39m\u001b[33marxiv_categories\u001b[39m\u001b[33m'\u001b[39m:\n\u001b[32m 187\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.arxiv_categories.update_category(**node_features)\n", - "\u001b[31mTypeError\u001b[39m: SQLArxivAuthors.update_author() missing 1 required positional argument: 'id'" + "name": "stdout", + "output_type": "stream", + "text": [ + "Author updated successfully!\n" ] } ], @@ -686,8 +669,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "./download/1806.08804v4/1806.08804v4.tar.gz\n", - "paper with id 1806.08804v4 downloaded\n", "{'id': '1903.03894v4', 'title': 'GNNExplainer: Generating Explanations for Graph Neural Networks', 'abstract': \"Graph Neural Networks (GNNs) are a powerful tool for machine learning on\\ngraphs.GNNs combine node feature information with the graph structure by\\nrecursively passing neural messages along edges of the input graph. However,\\nincorporating both graph structure and feature information leads to complex\\nmodels, and explaining predictions made by GNNs remains unsolved. Here we\\npropose GNNExplainer, the first general, model-agnostic approach for providing\\ninterpretable explanations for predictions of any GNN-based model on any\\ngraph-based machine learning task. Given an instance, GNNExplainer identifies a\\ncompact subgraph structure and a small subset of node features that have a\\ncrucial role in GNN's prediction. Further, GNNExplainer can generate consistent\\nand concise explanations for an entire class of instances. We formulate\\nGNNExplainer as an optimization task that maximizes the mutual information\\nbetween a GNN's prediction and distribution of possible subgraph structures.\\nExperiments on synthetic and real-world graphs show that our approach can\\nidentify important graph structures as well as node features, and outperforms\\nbaselines by 17.1% on average. GNNExplainer provides a variety of benefits,\\nfrom the ability to visualize semantically relevant structures to\\ninterpretability, to giving insights into errors of faulty GNNs.\", 'authors': ['Rex Ying', 'Dylan Bourgeois', 'Jiaxuan You', 'Marinka Zitnik', 'Jure Leskovec'], 'published': '2019-03-10 00:56:26+00:00', 'categories': ['cs.LG', 'stat.ML'], 'url': 'http://arxiv.org/abs/1903.03894v4'}\n", "{'id': '1806.08804v4', 'title': 'Hierarchical Graph Representation Learning with Differentiable Pooling', 'abstract': 'Recently, graph neural networks (GNNs) have revolutionized the field of graph\\nrepresentation learning through effectively learned node embeddings, and\\nachieved state-of-the-art results in tasks such as node classification and link\\nprediction. However, current GNN methods are inherently flat and do not learn\\nhierarchical representations of graphs---a limitation that is especially\\nproblematic for the task of graph classification, where the goal is to predict\\nthe label associated with an entire graph. Here we propose DiffPool, a\\ndifferentiable graph pooling module that can generate hierarchical\\nrepresentations of graphs and can be combined with various graph neural network\\narchitectures in an end-to-end fashion. DiffPool learns a differentiable soft\\ncluster assignment for nodes at each layer of a deep GNN, mapping nodes to a\\nset of clusters, which then form the coarsened input for the next GNN layer.\\nOur experimental results show that combining existing GNN methods with DiffPool\\nyields an average improvement of 5-10% accuracy on graph classification\\nbenchmarks, compared to all existing pooling approaches, achieving a new\\nstate-of-the-art on four out of five benchmark data sets.', 'authors': ['Rex Ying', 'Jiaxuan You', 'Christopher Morris', 'Xiang Ren', 'William L. Hamilton', 'Jure Leskovec'], 'published': '2018-06-22 18:04:46+00:00', 'categories': ['cs.LG', 'cs.NE', 'cs.SI', 'stat.ML'], 'url': 'http://arxiv.org/abs/1806.08804v4'}\n" ] @@ -866,16 +847,12 @@ "metadata": {}, "outputs": [ { - "ename": "UndefinedObject", - "evalue": "constraint \"ux_arxiv_figures_name_notnull\" for table \"arxiv_figures\" does not exist\n", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mUndefinedObject\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[26]\u001b[39m\u001b[32m, line 27\u001b[39m\n\u001b[32m 2\u001b[39m figures = [\n\u001b[32m 3\u001b[39m {\n\u001b[32m 4\u001b[39m \u001b[33m'\u001b[39m\u001b[33mpaper_arxiv_id\u001b[39m\u001b[33m'\u001b[39m: \u001b[33m'\u001b[39m\u001b[33m1706.03762v7\u001b[39m\u001b[33m'\u001b[39m,\n\u001b[32m (...)\u001b[39m\u001b[32m 23\u001b[39m }\n\u001b[32m 24\u001b[39m ]\n\u001b[32m 26\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m figure \u001b[38;5;129;01min\u001b[39;00m figures:\n\u001b[32m---> \u001b[39m\u001b[32m27\u001b[39m \u001b[43mresearch_arcade\u001b[49m\u001b[43m.\u001b[49m\u001b[43minsert_node\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43marxiv_figures\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnode_features\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfigure\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 28\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mInserted \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfigure[\u001b[33m'\u001b[39m\u001b[33mname\u001b[39m\u001b[33m'\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n", - "\u001b[36mFile \u001b[39m\u001b[32m/data/cl195/paper-crawler/research_arcade/research_arcade.py:128\u001b[39m, in \u001b[36mResearchArcade.insert_node\u001b[39m\u001b[34m(self, table, node_features)\u001b[39m\n\u001b[32m 126\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.arxiv_categories.insert_category(**node_features)\n\u001b[32m 127\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m table == \u001b[33m'\u001b[39m\u001b[33marxiv_figures\u001b[39m\u001b[33m'\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m128\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43marxiv_figures\u001b[49m\u001b[43m.\u001b[49m\u001b[43minsert_figure\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mnode_features\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 129\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m table == \u001b[33m'\u001b[39m\u001b[33marxiv_tables\u001b[39m\u001b[33m'\u001b[39m:\n\u001b[32m 130\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.arxiv_tables.insert_table(**node_features)\n", - "\u001b[36mFile \u001b[39m\u001b[32m/data/cl195/paper-crawler/research_arcade/sql_database/sql_arxiv_figures.py:80\u001b[39m, in \u001b[36mSQLArxivFigure.insert_figure\u001b[39m\u001b[34m(self, paper_arxiv_id, path, caption, label, name)\u001b[39m\n\u001b[32m 78\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m 79\u001b[39m cur = conn.cursor()\n\u001b[32m---> \u001b[39m\u001b[32m80\u001b[39m \u001b[43mcur\u001b[49m\u001b[43m.\u001b[49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 81\u001b[39m \u001b[38;5;250;43m \u001b[39;49m\u001b[33;43;03m\"\"\"\u001b[39;49;00m\n\u001b[32m 82\u001b[39m \u001b[33;43;03m INSERT INTO arxiv_figures (paper_arxiv_id, path, caption, label, name)\u001b[39;49;00m\n\u001b[32m 83\u001b[39m \u001b[33;43;03m VALUES (%s, %s, %s, %s, %s)\u001b[39;49;00m\n\u001b[32m 84\u001b[39m \u001b[33;43;03m ON CONFLICT ON CONSTRAINT ux_arxiv_figures_name_notnull DO NOTHING\u001b[39;49;00m\n\u001b[32m 85\u001b[39m \u001b[33;43;03m RETURNING id\u001b[39;49;00m\n\u001b[32m 86\u001b[39m \u001b[33;43;03m \"\"\"\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m 87\u001b[39m \u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mpaper_arxiv_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcaption\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 88\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 89\u001b[39m res = cur.fetchone()\n\u001b[32m 90\u001b[39m cur.close()\n", - "\u001b[31mUndefinedObject\u001b[39m: constraint \"ux_arxiv_figures_name_notnull\" for table \"arxiv_figures\" does not exist\n" + "name": "stdout", + "output_type": "stream", + "text": [ + "Inserted Figure 1\n", + "Inserted Figure 2\n", + "Inserted Figure 3\n" ] } ], @@ -925,14 +902,13 @@ "metadata": {}, "outputs": [ { - "ename": "TypeError", - "evalue": "object of type 'NoneType' has no len()", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mTypeError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[27]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m 1\u001b[39m figures_df = research_arcade.get_all_node_features(\u001b[33m\"\u001b[39m\u001b[33marxiv_figures\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mTotal figures: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mfigures_df\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n\u001b[32m 3\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[33mAll figures:\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 4\u001b[39m \u001b[38;5;28mprint\u001b[39m(figures_df[[\u001b[33m'\u001b[39m\u001b[33mname\u001b[39m\u001b[33m'\u001b[39m, \u001b[33m'\u001b[39m\u001b[33mcaption\u001b[39m\u001b[33m'\u001b[39m, \u001b[33m'\u001b[39m\u001b[33mlabel\u001b[39m\u001b[33m'\u001b[39m]])\n", - "\u001b[31mTypeError\u001b[39m: object of type 'NoneType' has no len()" + "name": "stdout", + "output_type": "stream", + "text": [ + "Total figures: 3\n", + "\n", + "All figures:\n", + "[(1, '1706.03762v7', '/figures/transformer_architecture.png', 'The Transformer model architecture. The left side shows the encoder stack and the right side shows the decoder stack.', 'fig:architecture', 'Figure 1'), (2, '1706.03762v7', '/figures/scaled_dot_product_attention.png', 'Scaled Dot-Product Attention and Multi-Head Attention mechanisms.', 'fig:attention', 'Figure 2'), (3, '1706.03762v7', '/figures/positional_encoding.png', 'Positional encoding visualization showing sine and cosine functions of different frequencies.', 'fig:positional', 'Figure 3')]\n" ] } ], @@ -940,7 +916,7 @@ "figures_df = research_arcade.get_all_node_features(\"arxiv_figures\")\n", "print(f\"Total figures: {len(figures_df)}\")\n", "print(\"\\nAll figures:\")\n", - "print(figures_df[['name', 'caption', 'label']])" + "print(figures_df)" ] }, { @@ -980,7 +956,7 @@ "seed: ['1903.03894v4']\n", "BFS_que.qsize(): 1\n", "current paper: 1903.03894v4\n", - "Thread 136281040344640 Processing 1903.03894v4\n" + "Thread 131687254062656 Processing 1903.03894v4\n" ] }, { @@ -998,20 +974,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "Thread 136281040344640 Finished processing 1903.03894v4 (1/999999999) Time elapsed: 1.19s\n", + "Thread 131687254062656 Finished processing 1903.03894v4 (1/999999999) Time elapsed: 1.11s\n", "'NoneType' object is not subscriptable\n", - "Thread 136281040344640 Failed to process 1903.03894v4\n", - "Thread 136284945086272 Finished processing 1 papers\n", - "Error: The file './download/output/1903.03894v4.json' was not found.\n", - "seed: ['1806.08804v4']\n", - "BFS_que.qsize(): 1\n", - "current paper: 1806.08804v4\n", - "Thread 136281040344640 Processing 1806.08804v4\n", - "Thread 136281040344640 Processing file paper-diffpool.tex\n", - "numbmer of citations in node info method: 0\n", - "Cannot find the bib file refs.bib\n", - "Thread 136281040344640 Finished processing 1806.08804v4 (1/999999999) Time elapsed: 1.68s\n", - "Thread 136284945086272 Finished processing 1 papers\n" + "Thread 131687254062656 Failed to process 1903.03894v4\n", + "Thread 131691059222336 Finished processing 1 papers\n", + "Error: The file './download/output/1903.03894v4.json' was not found.\n" ] } ], @@ -1169,7 +1136,7 @@ "seed: ['1903.03894v4']\n", "BFS_que.qsize(): 1\n", "current paper: 1903.03894v4\n", - "Thread 136281040344640 Processing 1903.03894v4\n" + "Thread 131687254062656 Processing 1903.03894v4\n" ] }, { @@ -1187,13 +1154,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "Thread 136281040344640 Finished processing 1903.03894v4 (1/999999999) Time elapsed: 1.04s\n", + "Thread 131687254062656 Finished processing 1903.03894v4 (1/999999999) Time elapsed: 1.23s\n", "'NoneType' object is not subscriptable\n", - "Thread 136281040344640 Failed to process 1903.03894v4\n", - "Thread 136284945086272 Finished processing 1 papers\n", - "Error: The file './download/output/1903.03894v4.json' was not found.\n", - "An unexpected error occurred: constraint \"ux_arxiv_figures_name_notnull\" for table \"arxiv_figures\" does not exist\n", - "\n" + "Thread 131687254062656 Failed to process 1903.03894v4\n", + "Thread 131691059222336 Finished processing 1 papers\n", + "Error: The file './download/output/1903.03894v4.json' was not found.\n" ] } ], @@ -1211,14 +1176,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "No new figures to import\n" + "Error: CSV file ./examples/csv_data/csv_arxiv_figures_example.csv does not exist.\n" ] } ], @@ -1236,14 +1201,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "No new figures to import\n" + "Error: JSON file ./examples/json_data/json_arxiv_figures_example.json does not exist.\n" ] } ], @@ -1262,7 +1227,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 35, "id": "insert-tables", "metadata": {}, "outputs": [ @@ -1317,7 +1282,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 36, "id": "get-all-tables", "metadata": {}, "outputs": [ @@ -1365,7 +1330,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 37, "id": "c1735fc6", "metadata": {}, "outputs": [ @@ -1376,7 +1341,7 @@ "seed: ['1903.03894v4']\n", "BFS_que.qsize(): 1\n", "current paper: 1903.03894v4\n", - "Thread 136281040344640 Processing 1903.03894v4\n" + "Thread 131687254062656 Processing 1903.03894v4\n" ] }, { @@ -1394,11 +1359,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "Thread 136281040344640 Finished processing 1903.03894v4 (1/999999999) Time elapsed: 1.04s\n", + "Thread 131687254062656 Finished processing 1903.03894v4 (1/999999999) Time elapsed: 1.10s\n", "'NoneType' object is not subscriptable\n", - "Thread 136281040344640 Failed to process 1903.03894v4\n", - "Thread 136284945086272 Finished processing 1 papers\n", - "Error: The file './download/output/1903.03894v4.json' was not found.\n" + "Thread 131687254062656 Failed to process 1903.03894v4\n", + "Thread 131691059222336 Finished processing 1 papers\n", + "Error: The file './download/output/1903.03894v4.json' was not found.\n", + "An unexpected error occurred: SQLArxivSections.insert_section() got an unexpected keyword argument 'is_appendix'\n" ] } ], @@ -1416,7 +1382,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 38, "metadata": {}, "outputs": [ { @@ -1441,7 +1407,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 39, "metadata": {}, "outputs": [ { @@ -1467,20 +1433,20 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 40, "id": "insert-sections", "metadata": {}, "outputs": [ { - "ename": "TypeError", - "evalue": "SQLArxivSections.insert_section() got an unexpected keyword argument 'appendix'", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mTypeError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[39]\u001b[39m\u001b[32m, line 48\u001b[39m\n\u001b[32m 2\u001b[39m sections = [\n\u001b[32m 3\u001b[39m {\n\u001b[32m 4\u001b[39m \u001b[33m'\u001b[39m\u001b[33mcontent\u001b[39m\u001b[33m'\u001b[39m: \u001b[33m'\u001b[39m\u001b[33mThe dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder...\u001b[39m\u001b[33m'\u001b[39m,\n\u001b[32m (...)\u001b[39m\u001b[32m 44\u001b[39m }\n\u001b[32m 45\u001b[39m ]\n\u001b[32m 47\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m section \u001b[38;5;129;01min\u001b[39;00m sections:\n\u001b[32m---> \u001b[39m\u001b[32m48\u001b[39m \u001b[43mresearch_arcade\u001b[49m\u001b[43m.\u001b[49m\u001b[43minsert_node\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43marxiv_sections\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnode_features\u001b[49m\u001b[43m=\u001b[49m\u001b[43msection\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 49\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mInserted section: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00msection[\u001b[33m'\u001b[39m\u001b[33mtitle\u001b[39m\u001b[33m'\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n", - "\u001b[36mFile \u001b[39m\u001b[32m/data/cl195/paper-crawler/research_arcade/research_arcade.py:136\u001b[39m, in \u001b[36mResearchArcade.insert_node\u001b[39m\u001b[34m(self, table, node_features)\u001b[39m\n\u001b[32m 134\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.arxiv_paragraphs.insert_paragraph(**node_features)\n\u001b[32m 135\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m table == \u001b[33m'\u001b[39m\u001b[33marxiv_sections\u001b[39m\u001b[33m'\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m136\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43marxiv_sections\u001b[49m\u001b[43m.\u001b[49m\u001b[43minsert_section\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mnode_features\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 137\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 138\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mTable \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtable\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m not found.\u001b[39m\u001b[33m\"\u001b[39m)\n", - "\u001b[31mTypeError\u001b[39m: SQLArxivSections.insert_section() got an unexpected keyword argument 'appendix'" + "name": "stdout", + "output_type": "stream", + "text": [ + "Inserted section: Introduction\n", + "Inserted section: Background\n", + "Inserted section: Model Architecture\n", + "Inserted section: Training\n", + "Inserted section: Results\n", + "Inserted section: Conclusion\n" ] } ], @@ -1554,10 +1520,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "Total sections: [(1, \"\\n\\\\label{sec:intro}\\nIn recent years there has been a surge of interest in developing graph neural networks (GNNs)---general deep learning architectures that can operate over graph structured data, such as social network data \\\\cite{hamilton2017inductive,kipf2017semi,Vel+2018} or graph-based representations of molecules \\\\cite{dai2016discriminative,Duv+2015,Gil+2017}.\\nThe general approach with GNNs is to view the underlying graph as a computation graph and learn neural network primitives that generate individual node embeddings by passing, transforming, and aggregating node feature information across the graph~\\\\cite{Gil+2017,hamilton2017inductive}.\\nThe generated node embeddings can then be used as input to any differentiable prediction layer, e.g., for node classification \\\\cite{hamilton2017inductive} or link prediction \\\\cite{Sch+2017}, and the whole model can be trained in an end-to-end fashion. \\n\\n\\n\\n\\nHowever, a major limitation of current GNN architectures is that they are inherently {\\\\em flat}\\\\em flat as they only propagate information across the edges of the graph and are unable to infer and aggregate the information in a \\\\textit{hierarchical} way. \\nFor example, in order to successfully encode the graph structure of organic molecules, one would ideally want to encode the local molecular structure (e.g., individual atoms and their direct bonds) as well as the coarse-grained structure of the molecular graph (e.g., groups of atoms and bonds representing functional units in a molecule).\\nThis lack of hierarchical structure is especially problematic for the task of graph classification, where the goal is to predict the label associated with an \\\\textit{entire graph}. When applying GNNs to graph classification, the standard approach is to generate embeddings for all the nodes in the graph and then to {\\\\em globally pool}\\\\em globally pool all these node embeddings together, e.g., using a simple summation or neural network that operates over sets \\\\cite{dai2016discriminative,Duv+2015,Gil+2017,Li+2016}. This global pooling approach ignores any hierarchical structure that might be present in the graph, and it prevents researchers from building effective GNN models for predictive tasks over entire graphs.\\n\\n\\n\\n\\n\\n\\n\\nHere we propose \\\\name, a differentiable graph pooling module that can be adapted to various graph neural network architectures in an hierarchical and end-to-end fashion (Figure~\\\\ref{fig:illustration}). \\n\\\\name allows for developing deeper GNN models that can learn to operate on hierarchical representations of a graph. We develop a graph analogue of the spatial pooling operation in CNNs \\\\cite{krizhevsky2012imagenet}, which allows for deep CNN architectures to iteratively operate on coarser and coarser representations of an image. The challenge in the GNN setting---compared to standard CNNs---is that graphs contain no natural notion of spatial locality, i.e., one cannot simply pool together all nodes in a ``$m \\\\times m$m \\\\times m patch'' on a graph, because the complex topological structure of graphs precludes any straightforward, deterministic definition of a ``patch''. Moreover, unlike image data, graph data sets often contain graphs with varying numbers of nodes and edges, which makes defining a general graph pooling operator even more challenging.\\n\\nIn order to solve the above challenges, we require a model that learns how to cluster together nodes to build a hierarchical multi-layer scaffold on top of the underlying graph. \\nOur approach \\\\name learns a differentiable soft assignment at each layer of a deep GNN, mapping nodes to a set of clusters based on their learned embeddings. \\nIn this framework, we generate deep GNNs by ``stacking'' GNN layers in a hierarchical fashion (Figure~\\\\ref{fig:illustration}): the input nodes at the layer $l$l GNN module correspond to the clusters learned at the layer $l-1$l-1 GNN module. \\nThus, each layer of \\\\name coarsens the input graph more and more, and \\\\name is able to generate a hierarchical representation of any input graph after training.\\nWe show that \\\\name\\\\ can be combined with various GNN approaches, resulting in an average 7\\\\% gain in accuracy and a new state of the art on four out of five benchmark graph classification tasks. \\nFinally, we show that \\\\name\\\\ can learn interpretable hierarchical clusters that correspond to well-defined communities in the input graphs.\\n\\\\cut{\\n\\\\jure{Say much much more about our method, how it works and why it is cool.}\\n\\n\\\\chris{Maybe add highlevel visualization of the idea.}\\n\\n\\\\jure{ I strongly agree, we need a Figure 1 with an illustration of the method.\\nFor example, a graph and then a hierarchical multi-layer scaffold on top of it.\\nWill, you are great at creating good figures. Do you want to give it a try?}\\n\\\\will{Yes, I will brainstorm with Rex today about a figure and put something together :)}\\n\\n\\\\chris{Quickly sketched this. I think it somehow explains the high-level idea. Guess the equations are too much.}\\n}\\n\\\\jure{Say much much more about our method, how it works and why it is cool.}Say much much more about our method, how it works and why it is cool.\\n\\n\\\\chris{Maybe add highlevel visualization of the idea.}Maybe add highlevel visualization of the idea.\\n\\n\\\\jure{ I strongly agree, we need a Figure 1 with an illustration of the method.\\nFor example, a graph and then a hierarchical multi-layer scaffold on top of it.\\nWill, you are great at creating good figures. Do you want to give it a try?} I strongly agree, we need a Figure 1 with an illustration of the method.\\nFor example, a graph and then a hierarchical multi-layer scaffold on top of it.\\nWill, you are great at creating good figures. Do you want to give it a try?\\n\\\\will{Yes, I will brainstorm with Rex today about a figure and put something together :)}Yes, I will brainstorm with Rex today about a figure and put something together :)\\n\\n\\\\chris{Quickly sketched this. I think it somehow explains the high-level idea. Guess the equations are too much.}Quickly sketched this. I think it somehow explains the high-level idea. Guess the equations are too much.\\n\\n\\n\\\\begin{figure}[t]\\\\begin{center}\\n \\\\includegraphics[scale=0.95]{figs/differentiable-pooling-V2.pdf}\\n \\\\caption{High-level illustration of our proposed method \\\\name. At each hierarchical layer, we run a GNN model to obtain embeddings of nodes. We then use these learned embeddings to cluster nodes together and run another GNN layer on this coarsened graph. This whole process is repeated for $L$ layers and we use the final output representation to classify the graph. }\\n \\\\label{fig:illustration}\\n \\\\end{center}\\n\\\\end{figure}\\n \\\\includegraphics[scale=0.95]{figs/differentiable-pooling-V2.pdf}\\n \\\\caption{High-level illustration of our proposed method \\\\name. At each hierarchical layer, we run a GNN model to obtain embeddings of nodes. We then use these learned embeddings to cluster nodes together and run another GNN layer on this coarsened graph. This whole process is repeated for $L$ layers and we use the final output representation to classify the graph. }\\n \\\\label{fig:illustration}\\n \\n\\n\\n\\n\", 'Introduction', False, '1806.08804v4'), (2, \"\\n\\nOur work builds upon a rich line of recent research on graph neural networks and graph classification. \\n\\n\\\\xhdr{General graph neural networks}General graph neural networks\\nA wide variety of graph neural network (GNN) models have been proposed in recent years, including methods inspired by convolutional neural networks \\\\cite{Bru+2014,Def+2015,Duv+2015,hamilton2017inductive,kipf2017semi,Lei+2017,niepert2016learning, Vel+2018}, recurrent neural networks \\\\cite{Li+2016}, recursive neural networks~\\\\cite{bianchini2001,Sca+2009} and loopy belief propagation \\\\cite{dai2016discriminative}. \\nMost of these approaches fit within the framework of ``neural message passing'' proposed by Gilmer \\\\etal~\\\\cite{Gil+2017}. \\nIn the message passing framework, a GNN is viewed as a message passing algorithm where node representations are iteratively computed from the features of their neighbor nodes using a differentiable aggregation function.\\nHamilton \\\\etal~\\\\cite{Ham+2017a} provides a conceptual review of recent advancements in this area, and Bronstein \\\\etal \\\\cite{bronstein2017geometric} outlines connections to spectral graph convolutions. \\n\\n\\\\xhdr{Graph classification with graph neural networks}Graph classification with graph neural networks\\nGNNs have been applied to a wide variety of tasks, including node classification \\\\cite{hamilton2017inductive,kipf2017semi}, link prediction \\\\cite{kipf2018}, graph classification \\\\cite{dai2016discriminative,Duv+2015,zhang2018end}, and chemoinformatics~\\\\cite{Mer+2005,Lus+2013,Fou+2017,Jin+2018,Sch+2017}. \\nIn the context of graph classification---the task that we study here---a major challenge in applying GNNs is going from node embeddings, which are the output of GNNs, to a representation of the entire graph. \\nCommon approaches to this problem include simply summing up or averaging all the node embeddings in a final layer \\\\cite{Duv+2015}, introducing a ``virtual node'' that is connected to all the nodes in the graph \\\\cite{Li+2016}, or aggregating the node embeddings using a deep learning architecture that operates over sets \\\\cite{Gil+2017}.\\nHowever, all of these methods have the limitation that they do not learn hierarchical representations (i.e., all the node embeddings are globally pooled together in a single layer), and thus are unable to capture the natural structures of many real-world graphs.\\nSome recent approaches have also proposed applying CNN architectures to the concatenation of all the node embeddings \\\\cite{niepert2016learning,zhang2018end}, but this requires a specifying (or learning) a canonical ordering over nodes, which is in general very difficult and equivalent to solving graph isomorphism. \\n\\nLastly, there are some recent works that learn hierarchical graph representations by combining GNNs with deterministic graph clustering algorithms \\\\cite{Def+2015,simonovsky2017dynamic,Fey+2018}, following a two-stage approach. However, unlike these previous approaches, we seek to {\\\\em learn}\\\\em learn the hierarchical structure in an end-to-end fashion, rather than relying on a deterministic graph clustering subroutine.\\n\\n\\n\\n\\\\cut{\\n% Will: Let's try to move some of this to related work\\n\\\\rex{need to be removed probably. there are some points i think are important which is not illustrated in related work}\\nTo achieve this task. recent works have made several attempts.\\nRecent attempts include the use of linearization of graphs. A graph can be linearized to a vector representation using ``canonical ordering'' \\\\cite{niepert2016learning}. Pooling or striding is easy to carry out on the 1D representation. However, it is non-trivial to specify a canonical ordering that also preserves graph structure. For example, nodes that are distant in graph might be adjacent to each other in the linearized representation.\\n\\nAnother intuitive pooling strategy is to pool using a graph clustering or coarsening strategy \\\\cite{safro2015advanced}, which can be applied hierarchically and produce coarser and coarser graphs.\\nHowever, a variety of clustering and coarsening strategies have been designed, and significant hand-engineering is required to find the best strategy for a specific classification task at hand.\\n}\\n\\\\rex{need to be removed probably. there are some points i think are important which is not illustrated in related work}need to be removed probably. there are some points i think are important which is not illustrated in related work\\nTo achieve this task. recent works have made several attempts.\\nRecent attempts include the use of linearization of graphs. A graph can be linearized to a vector representation using ``canonical ordering'' \\\\cite{niepert2016learning}. Pooling or striding is easy to carry out on the 1D representation. However, it is non-trivial to specify a canonical ordering that also preserves graph structure. For example, nodes that are distant in graph might be adjacent to each other in the linearized representation.\\n\\nAnother intuitive pooling strategy is to pool using a graph clustering or coarsening strategy \\\\cite{safro2015advanced}, which can be applied hierarchically and produce coarser and coarser graphs.\\nHowever, a variety of clustering and coarsening strategies have been designed, and significant hand-engineering is required to find the best strategy for a specific classification task at hand.\\n\\n\\n\\n\\n\\\\cut{\\n\\\\xhdr{Graph kernels}\\n\\nIn recent years, various graph kernels have been proposed, which\\nimplicitly or explicitly map graphs to a Hilbert space. Gärtner \\\\etal\\\\cite{Gae+2003}\\nand Kashima \\\\etal~\\\\cite{Kas+2003} simultaneously proposed\\ngraph kernels based on random walks, which count the number of walks\\ntwo graphs have in common. Since then, random walk kernels have been\\nstudied intensively, e.g., see~\\\\cite{Sug+2015}.\\nKernels based on shortest paths were first proposed in~\\\\cite{Borgwardt2005}.\\n\\nA different line in the development of graph kernels focused\\nparticularly on scalable graph kernels. These kernels are typically\\ncomputed efficiently by explicit feature maps, which allow to bypass\\nthe computation of a gram matrix, and allow applying scalable linear\\nclassification algorithms. Prominent examples are kernels based on\\nsubgraphs up to a fixed size, so called\\n{graphlets}~\\\\cite{She+2009}. Other approaches of this category encode\\nthe neighborhood of every node by different techniques, e.g.,\\nsee~\\\\cite{Hid+2009, Neu+2016}, and most notably the\\nWeisfeiler-Lehman subtree kernel~\\\\cite{She+2011} and its higher-order\\nvariants~\\\\cite{Mor+2017}. Subgraph and Weisfeiler-Lehman kernels have\\nbeen successfully employed within frameworks for smoothed and deep\\ngraph kernels~\\\\cite{Yan+2015,Yan+2015a}. Recent developments include\\nassignment-based approaches~\\\\cite{kriege2016valid,Nik+2017,Joh+2015} and\\nspectral approaches~\\\\cite{Kon+2016}. Although graph kernels have shown good performance in the past, they lack the ability to adapt to they give data distribution at hand, since they mostly rely on precomputed features. Moreover, they may not scale to large data sets due to the quadratic overhead to compute the gram matrix. \\n\\nNiepert \\\\etal~\\\\cite{niepert2016learning} extracted canonical vector representations of neighborhoods of nodes, based on heuristics such as the Weisfeiler-Lehman algorithm~\\\\cite{She+2011}, to compute a graph embeddings and then used neural networks for the classification task. Moreover, recently graph neural networks (GNN) for node and graph classification became popular. Most of these\\napproaches fit into the framework proposed by~\\\\cite{Gil+2017}. Here a GNN is viewed as a message passing approach where node\\nfeatures are iteratively computed from the features of the\\nnode's neighbors by using a differentialable neighborhood aggregation function. The parameters of this function are \\nlearned together with the parameters of the neural network used for the\\nclassification task in an end-to-end fashion. Up to now, naïve global mean pooling or precomputed clustering methods were applied to compute graph embeddings from the features of each single node.\\n\\nNotable instances of this model include Neural\\nFingerprints~\\\\cite{Duv+2015}, Gated Graph Neural\\nNetworks~\\\\cite{Li+2016}, and spectral approaches proposed\\nin~\\\\cite{Bru+2014,Def+2015,kipf2017semi}. Dai \\\\etal\\\\cite{dai2016discriminative}\\nproposed an approach inspired by mean-field inference\\nand Lei \\\\etal\\\\cite{Lei+2017} showed that the generated\\nfeature maps lie in the same Hilbert space as some popular graph\\nkernels and successfully applied them in the domain of\\nchemoinformatics~\\\\cite{Jin+2018}. In~\\\\cite{simonovsky2017dynamic} GNN were extended to include edge features and various precomputed pooling heuristics based on clustering methods were applied. Attention-based extensions were explored in~\\\\cite{Vel+2018}. In order to make GNNs scale to large graphs Hamilton \\\\etal\\\\cite{hamilton2017inductive} and Chen \\\\etal\\\\cite{Che+2018} devised stochastic versions of GNNs. Recently, a differentiable pooling mechanism to compute graph embeddings based on node features using differentiable sorting was proposed~\\\\cite{zhang2018end}.\\n\\nMoreover, GNNs were applied for protein-protein\\ninteraction prediction~\\\\cite{Fou+2017} and quantum interactions in\\nmolecules~\\\\cite{Sch+2017}. An approach for unsupervised learning based\\non GNNs was presented in~\\\\cite{Gar+2017}. Early\\napproaches were published in~\\\\cite{Mer+2005} and~\\\\cite{Sca+2009,\\nSca+2009a}. A survey can be found in~\\\\cite{Ham+2017a}.\\n\\n}\\n\\\\xhdr{Graph kernels}Graph kernels\\n\\nIn recent years, various graph kernels have been proposed, which\\nimplicitly or explicitly map graphs to a Hilbert space. Gärtner \\\\etal\\\\cite{Gae+2003}\\nand Kashima \\\\etal~\\\\cite{Kas+2003} simultaneously proposed\\ngraph kernels based on random walks, which count the number of walks\\ntwo graphs have in common. Since then, random walk kernels have been\\nstudied intensively, e.g., see~\\\\cite{Sug+2015}.\\nKernels based on shortest paths were first proposed in~\\\\cite{Borgwardt2005}.\\n\\nA different line in the development of graph kernels focused\\nparticularly on scalable graph kernels. These kernels are typically\\ncomputed efficiently by explicit feature maps, which allow to bypass\\nthe computation of a gram matrix, and allow applying scalable linear\\nclassification algorithms. Prominent examples are kernels based on\\nsubgraphs up to a fixed size, so called\\n{graphlets}graphlets~\\\\cite{She+2009}. Other approaches of this category encode\\nthe neighborhood of every node by different techniques, e.g.,\\nsee~\\\\cite{Hid+2009, Neu+2016}, and most notably the\\nWeisfeiler-Lehman subtree kernel~\\\\cite{She+2011} and its higher-order\\nvariants~\\\\cite{Mor+2017}. Subgraph and Weisfeiler-Lehman kernels have\\nbeen successfully employed within frameworks for smoothed and deep\\ngraph kernels~\\\\cite{Yan+2015,Yan+2015a}. Recent developments include\\nassignment-based approaches~\\\\cite{kriege2016valid,Nik+2017,Joh+2015} and\\nspectral approaches~\\\\cite{Kon+2016}. Although graph kernels have shown good performance in the past, they lack the ability to adapt to they give data distribution at hand, since they mostly rely on precomputed features. Moreover, they may not scale to large data sets due to the quadratic overhead to compute the gram matrix. \\n\\nNiepert \\\\etal~\\\\cite{niepert2016learning} extracted canonical vector representations of neighborhoods of nodes, based on heuristics such as the Weisfeiler-Lehman algorithm~\\\\cite{She+2011}, to compute a graph embeddings and then used neural networks for the classification task. Moreover, recently graph neural networks (GNN) for node and graph classification became popular. Most of these\\napproaches fit into the framework proposed by~\\\\cite{Gil+2017}. Here a GNN is viewed as a message passing approach where node\\nfeatures are iteratively computed from the features of the\\nnode's neighbors by using a differentialable neighborhood aggregation function. The parameters of this function are \\nlearned together with the parameters of the neural network used for the\\nclassification task in an end-to-end fashion. Up to now, naïve global mean pooling or precomputed clustering methods were applied to compute graph embeddings from the features of each single node.\\n\\nNotable instances of this model include Neural\\nFingerprints~\\\\cite{Duv+2015}, Gated Graph Neural\\nNetworks~\\\\cite{Li+2016}, and spectral approaches proposed\\nin~\\\\cite{Bru+2014,Def+2015,kipf2017semi}. Dai \\\\etal\\\\cite{dai2016discriminative}\\nproposed an approach inspired by mean-field inference\\nand Lei \\\\etal\\\\cite{Lei+2017} showed that the generated\\nfeature maps lie in the same Hilbert space as some popular graph\\nkernels and successfully applied them in the domain of\\nchemoinformatics~\\\\cite{Jin+2018}. In~\\\\cite{simonovsky2017dynamic} GNN were extended to include edge features and various precomputed pooling heuristics based on clustering methods were applied. Attention-based extensions were explored in~\\\\cite{Vel+2018}. In order to make GNNs scale to large graphs Hamilton \\\\etal\\\\cite{hamilton2017inductive} and Chen \\\\etal\\\\cite{Che+2018} devised stochastic versions of GNNs. Recently, a differentiable pooling mechanism to compute graph embeddings based on node features using differentiable sorting was proposed~\\\\cite{zhang2018end}.\\n\\nMoreover, GNNs were applied for protein-protein\\ninteraction prediction~\\\\cite{Fou+2017} and quantum interactions in\\nmolecules~\\\\cite{Sch+2017}. An approach for unsupervised learning based\\non GNNs was presented in~\\\\cite{Gar+2017}. Early\\napproaches were published in~\\\\cite{Mer+2005} and~\\\\cite{Sca+2009,\\nSca+2009a}. A survey can be found in~\\\\cite{Ham+2017a}.\\n\\n\\n\\n\\n\\n\\n\\n\", 'Related Work', False, '1806.08804v4'), (3, \"\\n\\\\label{sec:proposed}\\n\\nThe key idea of \\\\name is that it enables the construction of deep, multi-layer GNN models by providing a differentiable module to hierarchically pool graph nodes. \\nIn this section, we outline the \\\\name module and show how it is applied in a deep GNN architecture.\\n\\n\\n\\n\\\\subsection{Preliminaries}\\n\\\\label{sec:setting}\\n\\nWe represent a graph $G$G as $(A,F)$(A,F), where $A\\\\in \\\\{0, 1\\\\}^{n\\\\times n}$A\\\\in \\\\{0, 1\\\\}^{n\\\\times n}n\\\\times n is the adjacency matrix, and $F \\\\in \\\\mathbb{R}^{n\\\\times d}$F \\\\in \\\\mathbb{R}^{n\\\\times d}n\\\\times d is the node feature matrix assuming each node has $d$d features.\\\\footnote{We do not consider edge features, although one can easily extend the algorithm to support edge features using techniques introduced in \\\\cite{simonovsky2017dynamic}.}\\nGiven a set of labeled graphs $\\\\mathcal{D}=\\\\{(G_1,y_1),(G_2,y_2),...\\\\}$\\\\mathcal{D}=\\\\{(G_1,y_1),(G_2,y_2),...\\\\} where $y_i \\\\in \\\\mathcal{Y}$y_i \\\\in \\\\mathcal{Y} is the label corresponding to graph $G_i \\\\in \\\\mathcal{G}$G_i \\\\in \\\\mathcal{G}, the goal of graph classification is to learn a mapping $f : \\\\mathcal{G} \\\\to \\\\mathcal{Y}$f : \\\\mathcal{G} \\\\to \\\\mathcal{Y} that maps graphs to the set of labels. \\nThe challenge---compared to standard supervised machine learning setup---is that we need a way to extract useful feature vectors from these input graphs.\\nThat is, in order to apply standard machine learning methods for classification, e.g., neural networks, we need a procedure to convert each graph to an finite dimensional vector in $\\\\mathbb{R}^D$\\\\mathbb{R}^D.\\n\\n\\n\\n\\\\xhdr{Graph neural networks}Graph neural networks\\nIn this work, we build upon graph neural networks in order to learn useful representations for graph classification in an end-to-end fashion. \\nIn particular, we consider GNNs that employ the following general ``message-passing'' architecture:\\n\\\\begin{equation}\\\\label{eq:gnn}\\n H^{(k)} = M(A,H^{(k-1)};\\\\theta^{(k)}),\\n\\\\end{equation}\\\\begin{equation}\\\\label{eq:gnn}\\n H^{(k)} = M(A,H^{(k-1)};\\\\theta^{(k)}),\\n\\\\end{equation}\\\\label{eq:gnn}\\n H^{(k)}(k) = M(A,H^{(k-1)}(k-1);\\\\theta^{(k)}(k)),\\n\\nwhere $H^{(k)} \\\\in \\\\mathbb{R}^{n \\\\times d}$H^{(k)}(k) \\\\in \\\\mathbb{R}^{n \\\\times d}n \\\\times d are the node embeddings (i.e., ``messages'') computed after $k$k steps of the GNN and $M$M is the message propagation function, which depends on the adjacency matrix, trainable parameters $\\\\theta^{(k)}$\\\\theta^{(k)}(k), and the node embeddings $H^{(k-1)}$H^{(k-1)}(k-1) generated from the previous message-passing step.\\\\footnote{For notational convenience, we assume that the embedding dimension is $d$ for all $H^{(k)}$; however, in general this restriction is not necessary.}\\nThe input node embeddings $H^{(0)}$H^{(0)}(0) at the initial message-passing iteration $(k=1)$(k=1), are initialized using\\nthe node features on the graph, $H^{(0)} = F$H^{(0)}(0) = F. \\n\\nThere are many possible implementations of the propagation function $M$M \\\\cite{Gil+2017,hamilton2017inductive}.\\nFor example, one popular variant of GNNs---Kipf's \\\\etal \\\\cite{kipf2017semi} Graph Convolutional Networks (GCNs)---implements $M$M using a combination of linear transformations and ReLU non-linearities:\\n\\\\begin{equation}\\n \\\\label{eq:gcn}\\n H^{(k)} = M(A,H^{(k-1)};W^{(k)}) = \\\\textrm{ReLU}(\\\\tilde{D}^{-\\\\frac{1}{2}}\\\\tilde{A}\\\\tilde{D}^{-\\\\frac{1}{2}} H^{(k-1)} W^{(k-1)}),\\n\\\\end{equation}\\\\begin{equation}\\n \\\\label{eq:gcn}\\n H^{(k)} = M(A,H^{(k-1)};W^{(k)}) = \\\\textrm{ReLU}(\\\\tilde{D}^{-\\\\frac{1}{2}}\\\\tilde{A}\\\\tilde{D}^{-\\\\frac{1}{2}} H^{(k-1)} W^{(k-1)}),\\n\\\\end{equation}\\n \\\\label{eq:gcn}\\n H^{(k)}(k) = M(A,H^{(k-1)}(k-1);W^{(k)}(k)) = \\\\textrm{ReLU}(\\\\tilde{D}^{-\\\\frac{1}{2}}-\\\\frac{1}{2}\\\\tilde{A}\\\\tilde{D}^{-\\\\frac{1}{2}}-\\\\frac{1}{2} H^{(k-1)}(k-1) W^{(k-1)}(k-1)),\\n\\nwhere $\\\\tilde{A}=A+I$\\\\tilde{A}=A+I, $\\\\tilde{D}=\\\\sum_j\\\\tilde{A}_{ij}$\\\\tilde{D}=\\\\sum_j\\\\tilde{A}_{ij}ij and $W^{(k)} \\\\in \\\\mathbb{R}^{d \\\\times d}$W^{(k)}(k) \\\\in \\\\mathbb{R}^{d \\\\times d}d \\\\times d is a trainable weight matrix.\\nThe differentiable pooling model we propose can be applied to any GNN model implementing Equation \\\\eqref{eq:gnn}, and is agnostic with regards to the specifics of how $M$M is implemented. \\n\\nA full GNN module will run $K$K iterations of Equation \\\\eqref{eq:gnn} to generate the final output node embeddings $Z=H^{(K)} \\\\in \\\\mathbb{R}^{n \\\\times d}$Z=H^{(K)}(K) \\\\in \\\\mathbb{R}^{n \\\\times d}n \\\\times d, where $K$K is usually in the range 2-6.\\nFor simplicity, in the following sections we will abstract away the internal structure of the GNNs and use $Z = \\\\gnn(A, X)$Z = \\\\gnn(A, X) to denote an arbitrary GNN module implementing $K$K iterations of message passing according to some adjacency matrix $A$A and initial input node features $X$X. \\n\\n\\\\xhdr{Stacking GNNs and pooling layers}Stacking GNNs and pooling layers\\nGNNs implementing Equation \\\\eqref{eq:gnn} are inherently flat, as they only propagate information across edges of a graph.\\nThe goal of this work is to define a general, end-to-end differentiable strategy that allows one to {\\\\em stack}\\\\em stack multiple GNN modules in a hierarchical fashion. \\nFormally, given $Z = \\\\gnn(A, X)$Z = \\\\gnn(A, X), the output of a GNN module, and a graph adjacency matrix $A \\\\in \\\\mathbb{R}^{n \\\\times n}$A \\\\in \\\\mathbb{R}^{n \\\\times n}n \\\\times n, we seek to define a strategy to output a new coarsened graph containing $m \u001b[39m\u001b[32m2\u001b[39m \u001b[43mresearch_arcade\u001b[49m\u001b[43m.\u001b[49m\u001b[43mconstruct_table_from_api\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43marxiv_paragraphs\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m/data/cl195/paper-crawler/research_arcade/research_arcade.py:585\u001b[39m, in \u001b[36mResearchArcade.construct_table_from_api\u001b[39m\u001b[34m(self, table, config)\u001b[39m\n\u001b[32m 583\u001b[39m \u001b[38;5;28mself\u001b[39m.arxiv_sections.construct_sections_table_from_api(**config)\n\u001b[32m 584\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m table == \u001b[33m\"\u001b[39m\u001b[33marxiv_paragraphs\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m585\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43marxiv_paragraphs\u001b[49m\u001b[43m.\u001b[49m\u001b[43mconstruct_paragraphs_table_from_api\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 586\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m table == \u001b[33m\"\u001b[39m\u001b[33marxiv_categories\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m 587\u001b[39m \u001b[38;5;28mself\u001b[39m.arxiv_categories.construct_category_table_from_api(**config)\n", - "\u001b[36mFile \u001b[39m\u001b[32m/data/cl195/paper-crawler/research_arcade/sql_database/sql_arxiv_paragraphs.py:501\u001b[39m, in \u001b[36mSQLArxivParagraphs.construct_paragraphs_table_from_api\u001b[39m\u001b[34m(self, arxiv_ids, dest_dir)\u001b[39m\n\u001b[32m 499\u001b[39m id_number = get_paragraph_num(paragraph_id)\n\u001b[32m 500\u001b[39m id_zero_based = id_number - section_min_paragraph[(paper_arxiv_id, paper_section)]\n\u001b[32m--> \u001b[39m\u001b[32m501\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43minsert_paragraph\u001b[49m\u001b[43m(\u001b[49m\u001b[43mparagraph_id\u001b[49m\u001b[43m=\u001b[49m\u001b[43mid_zero_based\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcontent\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcontent\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpaper_arxiv_id\u001b[49m\u001b[43m=\u001b[49m\u001b[43mpaper_arxiv_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpaper_section\u001b[49m\u001b[43m=\u001b[49m\u001b[43mpaper_section\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m/data/cl195/paper-crawler/research_arcade/sql_database/sql_arxiv_paragraphs.py:77\u001b[39m, in \u001b[36mSQLArxivParagraphs.insert_paragraph\u001b[39m\u001b[34m(self, paragraph_id, content, paper_arxiv_id, paper_section, section_id, paragraph_in_paper_id)\u001b[39m\n\u001b[32m 75\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m 76\u001b[39m cur = conn.cursor()\n\u001b[32m---> \u001b[39m\u001b[32m77\u001b[39m \u001b[43mcur\u001b[49m\u001b[43m.\u001b[49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 78\u001b[39m \u001b[38;5;250;43m \u001b[39;49m\u001b[33;43;03m\"\"\"\u001b[39;49;00m\n\u001b[32m 79\u001b[39m \u001b[33;43;03m INSERT INTO arxiv_paragraphs (paragraph_id, content, paper_arxiv_id, paper_section)\u001b[39;49;00m\n\u001b[32m 80\u001b[39m \u001b[33;43;03m VALUES (%s, %s, %s, %s)\u001b[39;49;00m\n\u001b[32m 81\u001b[39m \u001b[33;43;03m ON CONFLICT ON CONSTRAINT ux_arxiv_paragraphs_unique DO NOTHING\u001b[39;49;00m\n\u001b[32m 82\u001b[39m \u001b[33;43;03m RETURNING id\u001b[39;49;00m\n\u001b[32m 83\u001b[39m \u001b[33;43;03m \"\"\"\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m 84\u001b[39m \u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mparagraph_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcontent\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpaper_arxiv_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpaper_section\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 85\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 86\u001b[39m res = cur.fetchone()\n\u001b[32m 87\u001b[39m cur.close()\n", - "\u001b[31mUndefinedObject\u001b[39m: constraint \"ux_arxiv_paragraphs_unique\" for table \"arxiv_paragraphs\" does not exist\n" - ] } ], "source": [ @@ -1731,14 +1683,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 44, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "No new paragraphs to import (all paragraphs already exist)\n" + "Error: JSON file ./examples/json_data/json_arxiv_paragraphs_example.json does not exist.\n" ] } ], @@ -1757,21 +1709,19 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 45, "id": "insert-paragraphs", "metadata": {}, "outputs": [ { - "ename": "UndefinedObject", - "evalue": "constraint \"ux_arxiv_paragraphs_unique\" for table \"arxiv_paragraphs\" does not exist\n", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mUndefinedObject\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[44]\u001b[39m\u001b[32m, line 46\u001b[39m\n\u001b[32m 2\u001b[39m paragraphs = [\n\u001b[32m 3\u001b[39m {\n\u001b[32m 4\u001b[39m \u001b[33m'\u001b[39m\u001b[33mparagraph_id\u001b[39m\u001b[33m'\u001b[39m: \u001b[32m1\u001b[39m,\n\u001b[32m (...)\u001b[39m\u001b[32m 42\u001b[39m }\n\u001b[32m 43\u001b[39m ]\n\u001b[32m 45\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m paragraph \u001b[38;5;129;01min\u001b[39;00m paragraphs:\n\u001b[32m---> \u001b[39m\u001b[32m46\u001b[39m \u001b[43mresearch_arcade\u001b[49m\u001b[43m.\u001b[49m\u001b[43minsert_node\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43marxiv_paragraphs\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnode_features\u001b[49m\u001b[43m=\u001b[49m\u001b[43mparagraph\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 47\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mInserted paragraph \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mparagraph[\u001b[33m'\u001b[39m\u001b[33mparagraph_id\u001b[39m\u001b[33m'\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m from \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mparagraph[\u001b[33m'\u001b[39m\u001b[33mpaper_section\u001b[39m\u001b[33m'\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n", - "\u001b[36mFile \u001b[39m\u001b[32m/data/cl195/paper-crawler/research_arcade/research_arcade.py:134\u001b[39m, in \u001b[36mResearchArcade.insert_node\u001b[39m\u001b[34m(self, table, node_features)\u001b[39m\n\u001b[32m 132\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.arxiv_papers.insert_paper(**node_features)\n\u001b[32m 133\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m table == \u001b[33m'\u001b[39m\u001b[33marxiv_paragraphs\u001b[39m\u001b[33m'\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m134\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43marxiv_paragraphs\u001b[49m\u001b[43m.\u001b[49m\u001b[43minsert_paragraph\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mnode_features\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 135\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m table == \u001b[33m'\u001b[39m\u001b[33marxiv_sections\u001b[39m\u001b[33m'\u001b[39m:\n\u001b[32m 136\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.arxiv_sections.insert_section(**node_features)\n", - "\u001b[36mFile \u001b[39m\u001b[32m/data/cl195/paper-crawler/research_arcade/sql_database/sql_arxiv_paragraphs.py:77\u001b[39m, in \u001b[36mSQLArxivParagraphs.insert_paragraph\u001b[39m\u001b[34m(self, paragraph_id, content, paper_arxiv_id, paper_section, section_id, paragraph_in_paper_id)\u001b[39m\n\u001b[32m 75\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m 76\u001b[39m cur = conn.cursor()\n\u001b[32m---> \u001b[39m\u001b[32m77\u001b[39m \u001b[43mcur\u001b[49m\u001b[43m.\u001b[49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 78\u001b[39m \u001b[38;5;250;43m \u001b[39;49m\u001b[33;43;03m\"\"\"\u001b[39;49;00m\n\u001b[32m 79\u001b[39m \u001b[33;43;03m INSERT INTO arxiv_paragraphs (paragraph_id, content, paper_arxiv_id, paper_section)\u001b[39;49;00m\n\u001b[32m 80\u001b[39m \u001b[33;43;03m VALUES (%s, %s, %s, %s)\u001b[39;49;00m\n\u001b[32m 81\u001b[39m \u001b[33;43;03m ON CONFLICT ON CONSTRAINT ux_arxiv_paragraphs_unique DO NOTHING\u001b[39;49;00m\n\u001b[32m 82\u001b[39m \u001b[33;43;03m RETURNING id\u001b[39;49;00m\n\u001b[32m 83\u001b[39m \u001b[33;43;03m \"\"\"\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m 84\u001b[39m \u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mparagraph_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcontent\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpaper_arxiv_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpaper_section\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 85\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 86\u001b[39m res = cur.fetchone()\n\u001b[32m 87\u001b[39m cur.close()\n", - "\u001b[31mUndefinedObject\u001b[39m: constraint \"ux_arxiv_paragraphs_unique\" for table \"arxiv_paragraphs\" does not exist\n" + "name": "stdout", + "output_type": "stream", + "text": [ + "Inserted paragraph 1 from Introduction\n", + "Inserted paragraph 2 from Introduction\n", + "Inserted paragraph 3 from Introduction\n", + "Inserted paragraph 4 from Introduction\n", + "Inserted paragraph 5 from Introduction\n" ] } ], @@ -1835,19 +1785,18 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 46, "id": "get-all-paragraphs", "metadata": {}, "outputs": [ { - "ename": "TypeError", - "evalue": "object of type 'NoneType' has no len()", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mTypeError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[45]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m 1\u001b[39m paragraphs_df = research_arcade.get_all_node_features(\u001b[33m\"\u001b[39m\u001b[33marxiv_paragraphs\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mTotal paragraphs: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mparagraphs_df\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n\u001b[32m 3\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[33mFirst 3 paragraphs:\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 4\u001b[39m \u001b[38;5;28mprint\u001b[39m(paragraphs_df[[\u001b[33m'\u001b[39m\u001b[33mparagraph_id\u001b[39m\u001b[33m'\u001b[39m, \u001b[33m'\u001b[39m\u001b[33mpaper_section\u001b[39m\u001b[33m'\u001b[39m, \u001b[33m'\u001b[39m\u001b[33mcontent\u001b[39m\u001b[33m'\u001b[39m]].head(\u001b[32m3\u001b[39m))\n", - "\u001b[31mTypeError\u001b[39m: object of type 'NoneType' has no len()" + "name": "stdout", + "output_type": "stream", + "text": [ + "Total paragraphs: 115\n", + "\n", + "First 3 paragraphs:\n", + "[(1, '0', '\\\\label{sec:intro}\\nIn recent years there has been a surge of interest in developing graph neural networks (GNNs)---general deep learning architectures that can operate over graph structured data, such as social network data \\\\cite{hamilton2017inductive,kipf2017semi,Vel+2018} or graph-based representations of molecules \\\\cite{dai2016discriminative,Duv+2015,Gil+2017}.\\nThe general approach with GNNs is to view the underlying graph as a computation graph and learn neural network primitives that generate individual node embeddings by passing, transforming, and aggregating node feature information across the graph~\\\\cite{Gil+2017,hamilton2017inductive}.\\nThe generated node embeddings can then be used as input to any differentiable prediction layer, e.g., for node classification \\\\cite{hamilton2017inductive} or link prediction \\\\cite{Sch+2017}, and the whole model can be trained in an end-to-end fashion.', '1806.08804v4', 'Introduction'), (2, '1', 'However, a major limitation of current GNN architectures is that they are inherently {\\\\em flat}\\\\em flat as they only propagate information across the edges of the graph and are unable to infer and aggregate the information in a \\\\textit{hierarchical} way. \\nFor example, in order to successfully encode the graph structure of organic molecules, one would ideally want to encode the local molecular structure (e.g., individual atoms and their direct bonds) as well as the coarse-grained structure of the molecular graph (e.g., groups of atoms and bonds representing functional units in a molecule).\\nThis lack of hierarchical structure is especially problematic for the task of graph classification, where the goal is to predict the label associated with an \\\\textit{entire graph}. When applying GNNs to graph classification, the standard approach is to generate embeddings for all the nodes in the graph and then to {\\\\em globally pool}\\\\em globally pool all these node embeddings together, e.g., using a simple summation or neural network that operates over sets \\\\cite{dai2016discriminative,Duv+2015,Gil+2017,Li+2016}. This global pooling approach ignores any hierarchical structure that might be present in the graph, and it prevents researchers from building effective GNN models for predictive tasks over entire graphs.', '1806.08804v4', 'Introduction'), (3, '2', \"Here we propose \\\\name, a differentiable graph pooling module that can be adapted to various graph neural network architectures in an hierarchical and end-to-end fashion (Figure~\\\\ref{fig:illustration}). \\n\\\\name allows for developing deeper GNN models that can learn to operate on hierarchical representations of a graph. We develop a graph analogue of the spatial pooling operation in CNNs \\\\cite{krizhevsky2012imagenet}, which allows for deep CNN architectures to iteratively operate on coarser and coarser representations of an image. The challenge in the GNN setting---compared to standard CNNs---is that graphs contain no natural notion of spatial locality, i.e., one cannot simply pool together all nodes in a ``$m \\\\times m$m \\\\times m patch'' on a graph, because the complex topological structure of graphs precludes any straightforward, deterministic definition of a ``patch''. Moreover, unlike image data, graph data sets often contain graphs with varying numbers of nodes and edges, which makes defining a general graph pooling operator even more challenging.\", '1806.08804v4', 'Introduction'), (4, '3', \"In order to solve the above challenges, we require a model that learns how to cluster together nodes to build a hierarchical multi-layer scaffold on top of the underlying graph. \\nOur approach \\\\name learns a differentiable soft assignment at each layer of a deep GNN, mapping nodes to a set of clusters based on their learned embeddings. \\nIn this framework, we generate deep GNNs by ``stacking'' GNN layers in a hierarchical fashion (Figure~\\\\ref{fig:illustration}): the input nodes at the layer $l$l GNN module correspond to the clusters learned at the layer $l-1$l-1 GNN module. \\nThus, each layer of \\\\name coarsens the input graph more and more, and \\\\name is able to generate a hierarchical representation of any input graph after training.\\nWe show that \\\\name\\\\ can be combined with various GNN approaches, resulting in an average 7\\\\% gain in accuracy and a new state of the art on four out of five benchmark graph classification tasks. \\nFinally, we show that \\\\name\\\\ can learn interpretable hierarchical clusters that correspond to well-defined communities in the input graphs.\\n\\\\cut{\\n\\\\jure{Say much much more about our method, how it works and why it is cool.}\", '1806.08804v4', 'Introduction'), (5, '4', '\\\\chris{Maybe add highlevel visualization of the idea.}', '1806.08804v4', 'Introduction'), (6, '5', '\\\\jure{ I strongly agree, we need a Figure 1 with an illustration of the method.\\nFor example, a graph and then a hierarchical multi-layer scaffold on top of it.\\nWill, you are great at creating good figures. Do you want to give it a try?}\\n\\\\will{Yes, I will brainstorm with Rex today about a figure and put something together :)}', '1806.08804v4', 'Introduction'), (7, '6', '\\\\chris{Quickly sketched this. I think it somehow explains the high-level idea. Guess the equations are too much.}\\n}\\n\\\\jure{Say much much more about our method, how it works and why it is cool.}Say much much more about our method, how it works and why it is cool.', '1806.08804v4', 'Introduction'), (8, '7', '\\\\chris{Maybe add highlevel visualization of the idea.}Maybe add highlevel visualization of the idea.', '1806.08804v4', 'Introduction'), (9, '8', '\\\\jure{ I strongly agree, we need a Figure 1 with an illustration of the method.\\nFor example, a graph and then a hierarchical multi-layer scaffold on top of it.\\nWill, you are great at creating good figures. Do you want to give it a try?} I strongly agree, we need a Figure 1 with an illustration of the method.\\nFor example, a graph and then a hierarchical multi-layer scaffold on top of it.\\nWill, you are great at creating good figures. Do you want to give it a try?\\n\\\\will{Yes, I will brainstorm with Rex today about a figure and put something together :)}Yes, I will brainstorm with Rex today about a figure and put something together :)', '1806.08804v4', 'Introduction'), (10, '9', '\\\\chris{Quickly sketched this. I think it somehow explains the high-level idea. Guess the equations are too much.}Quickly sketched this. I think it somehow explains the high-level idea. Guess the equations are too much.', '1806.08804v4', 'Introduction'), (11, '0', 'Our work builds upon a rich line of recent research on graph neural networks and graph classification.', '1806.08804v4', 'Related Work'), (12, '1', \"\\\\xhdr{General graph neural networks}General graph neural networks\\nA wide variety of graph neural network (GNN) models have been proposed in recent years, including methods inspired by convolutional neural networks \\\\cite{Bru+2014,Def+2015,Duv+2015,hamilton2017inductive,kipf2017semi,Lei+2017,niepert2016learning, Vel+2018}, recurrent neural networks \\\\cite{Li+2016}, recursive neural networks~\\\\cite{bianchini2001,Sca+2009} and loopy belief propagation \\\\cite{dai2016discriminative}. \\nMost of these approaches fit within the framework of ``neural message passing'' proposed by Gilmer \\\\etal~\\\\cite{Gil+2017}. \\nIn the message passing framework, a GNN is viewed as a message passing algorithm where node representations are iteratively computed from the features of their neighbor nodes using a differentiable aggregation function.\\nHamilton \\\\etal~\\\\cite{Ham+2017a} provides a conceptual review of recent advancements in this area, and Bronstein \\\\etal \\\\cite{bronstein2017geometric} outlines connections to spectral graph convolutions.\", '1806.08804v4', 'Related Work'), (13, '2', \"\\\\xhdr{Graph classification with graph neural networks}Graph classification with graph neural networks\\nGNNs have been applied to a wide variety of tasks, including node classification \\\\cite{hamilton2017inductive,kipf2017semi}, link prediction \\\\cite{kipf2018}, graph classification \\\\cite{dai2016discriminative,Duv+2015,zhang2018end}, and chemoinformatics~\\\\cite{Mer+2005,Lus+2013,Fou+2017,Jin+2018,Sch+2017}. \\nIn the context of graph classification---the task that we study here---a major challenge in applying GNNs is going from node embeddings, which are the output of GNNs, to a representation of the entire graph. \\nCommon approaches to this problem include simply summing up or averaging all the node embeddings in a final layer \\\\cite{Duv+2015}, introducing a ``virtual node'' that is connected to all the nodes in the graph \\\\cite{Li+2016}, or aggregating the node embeddings using a deep learning architecture that operates over sets \\\\cite{Gil+2017}.\\nHowever, all of these methods have the limitation that they do not learn hierarchical representations (i.e., all the node embeddings are globally pooled together in a single layer), and thus are unable to capture the natural structures of many real-world graphs.\\nSome recent approaches have also proposed applying CNN architectures to the concatenation of all the node embeddings \\\\cite{niepert2016learning,zhang2018end}, but this requires a specifying (or learning) a canonical ordering over nodes, which is in general very difficult and equivalent to solving graph isomorphism.\", '1806.08804v4', 'Related Work'), (14, '3', 'Lastly, there are some recent works that learn hierarchical graph representations by combining GNNs with deterministic graph clustering algorithms \\\\cite{Def+2015,simonovsky2017dynamic,Fey+2018}, following a two-stage approach. However, unlike these previous approaches, we seek to {\\\\em learn}\\\\em learn the hierarchical structure in an end-to-end fashion, rather than relying on a deterministic graph clustering subroutine.', '1806.08804v4', 'Related Work'), (15, '4', \"\\\\cut{\\n% Will: Let's try to move some of this to related work\\n\\\\rex{need to be removed probably. there are some points i think are important which is not illustrated in related work}\\nTo achieve this task. recent works have made several attempts.\\nRecent attempts include the use of linearization of graphs. A graph can be linearized to a vector representation using ``canonical ordering'' \\\\cite{niepert2016learning}. Pooling or striding is easy to carry out on the 1D representation. However, it is non-trivial to specify a canonical ordering that also preserves graph structure. For example, nodes that are distant in graph might be adjacent to each other in the linearized representation.\", '1806.08804v4', 'Related Work'), (16, '5', \"Another intuitive pooling strategy is to pool using a graph clustering or coarsening strategy \\\\cite{safro2015advanced}, which can be applied hierarchically and produce coarser and coarser graphs.\\nHowever, a variety of clustering and coarsening strategies have been designed, and significant hand-engineering is required to find the best strategy for a specific classification task at hand.\\n}\\n\\\\rex{need to be removed probably. there are some points i think are important which is not illustrated in related work}need to be removed probably. there are some points i think are important which is not illustrated in related work\\nTo achieve this task. recent works have made several attempts.\\nRecent attempts include the use of linearization of graphs. A graph can be linearized to a vector representation using ``canonical ordering'' \\\\cite{niepert2016learning}. Pooling or striding is easy to carry out on the 1D representation. However, it is non-trivial to specify a canonical ordering that also preserves graph structure. For example, nodes that are distant in graph might be adjacent to each other in the linearized representation.\", '1806.08804v4', 'Related Work'), (17, '6', 'Another intuitive pooling strategy is to pool using a graph clustering or coarsening strategy \\\\cite{safro2015advanced}, which can be applied hierarchically and produce coarser and coarser graphs.\\nHowever, a variety of clustering and coarsening strategies have been designed, and significant hand-engineering is required to find the best strategy for a specific classification task at hand.', '1806.08804v4', 'Related Work'), (18, '7', '\\\\cut{\\n\\\\xhdr{Graph kernels}', '1806.08804v4', 'Related Work'), (19, '8', 'In recent years, various graph kernels have been proposed, which\\nimplicitly or explicitly map graphs to a Hilbert space. Gärtner \\\\etal\\\\cite{Gae+2003}\\nand Kashima \\\\etal~\\\\cite{Kas+2003} simultaneously proposed\\ngraph kernels based on random walks, which count the number of walks\\ntwo graphs have in common. Since then, random walk kernels have been\\nstudied intensively, e.g., see~\\\\cite{Sug+2015}.\\nKernels based on shortest paths were first proposed in~\\\\cite{Borgwardt2005}.', '1806.08804v4', 'Related Work'), (20, '9', 'A different line in the development of graph kernels focused\\nparticularly on scalable graph kernels. These kernels are typically\\ncomputed efficiently by explicit feature maps, which allow to bypass\\nthe computation of a gram matrix, and allow applying scalable linear\\nclassification algorithms. Prominent examples are kernels based on\\nsubgraphs up to a fixed size, so called\\n{graphlets}~\\\\cite{She+2009}. Other approaches of this category encode\\nthe neighborhood of every node by different techniques, e.g.,\\nsee~\\\\cite{Hid+2009, Neu+2016}, and most notably the\\nWeisfeiler-Lehman subtree kernel~\\\\cite{She+2011} and its higher-order\\nvariants~\\\\cite{Mor+2017}. Subgraph and Weisfeiler-Lehman kernels have\\nbeen successfully employed within frameworks for smoothed and deep\\ngraph kernels~\\\\cite{Yan+2015,Yan+2015a}. Recent developments include\\nassignment-based approaches~\\\\cite{kriege2016valid,Nik+2017,Joh+2015} and\\nspectral approaches~\\\\cite{Kon+2016}. Although graph kernels have shown good performance in the past, they lack the ability to adapt to they give data distribution at hand, since they mostly rely on precomputed features. Moreover, they may not scale to large data sets due to the quadratic overhead to compute the gram matrix.', '1806.08804v4', 'Related Work'), (21, '10', \"Niepert \\\\etal~\\\\cite{niepert2016learning} extracted canonical vector representations of neighborhoods of nodes, based on heuristics such as the Weisfeiler-Lehman algorithm~\\\\cite{She+2011}, to compute a graph embeddings and then used neural networks for the classification task. Moreover, recently graph neural networks (GNN) for node and graph classification became popular. Most of these\\napproaches fit into the framework proposed by~\\\\cite{Gil+2017}. Here a GNN is viewed as a message passing approach where node\\nfeatures are iteratively computed from the features of the\\nnode's neighbors by using a differentialable neighborhood aggregation function. The parameters of this function are \\nlearned together with the parameters of the neural network used for the\\nclassification task in an end-to-end fashion. Up to now, naïve global mean pooling or precomputed clustering methods were applied to compute graph embeddings from the features of each single node.\", '1806.08804v4', 'Related Work'), (34, '4', \"\\\\xhdr{Graph neural networks}Graph neural networks\\nIn this work, we build upon graph neural networks in order to learn useful representations for graph classification in an end-to-end fashion. \\nIn particular, we consider GNNs that employ the following general ``message-passing'' architecture:\\n\\\\begin{equation}\\\\label{eq:gnn}\\n H^{(k)} = M(A,H^{(k-1)};\\\\theta^{(k)}),\\n\\\\end{equation}\\\\begin{equation}\\\\label{eq:gnn}\\n H^{(k)} = M(A,H^{(k-1)};\\\\theta^{(k)}),\\n\\\\end{equation}\\\\label{eq:gnn}\\n H^{(k)}(k) = M(A,H^{(k-1)}(k-1);\\\\theta^{(k)}(k)),\", '1806.08804v4', 'Proposed Method'), (22, '11', 'Notable instances of this model include Neural\\nFingerprints~\\\\cite{Duv+2015}, Gated Graph Neural\\nNetworks~\\\\cite{Li+2016}, and spectral approaches proposed\\nin~\\\\cite{Bru+2014,Def+2015,kipf2017semi}. Dai \\\\etal\\\\cite{dai2016discriminative}\\nproposed an approach inspired by mean-field inference\\nand Lei \\\\etal\\\\cite{Lei+2017} showed that the generated\\nfeature maps lie in the same Hilbert space as some popular graph\\nkernels and successfully applied them in the domain of\\nchemoinformatics~\\\\cite{Jin+2018}. In~\\\\cite{simonovsky2017dynamic} GNN were extended to include edge features and various precomputed pooling heuristics based on clustering methods were applied. Attention-based extensions were explored in~\\\\cite{Vel+2018}. In order to make GNNs scale to large graphs Hamilton \\\\etal\\\\cite{hamilton2017inductive} and Chen \\\\etal\\\\cite{Che+2018} devised stochastic versions of GNNs. Recently, a differentiable pooling mechanism to compute graph embeddings based on node features using differentiable sorting was proposed~\\\\cite{zhang2018end}.', '1806.08804v4', 'Related Work'), (23, '12', 'Moreover, GNNs were applied for protein-protein\\ninteraction prediction~\\\\cite{Fou+2017} and quantum interactions in\\nmolecules~\\\\cite{Sch+2017}. An approach for unsupervised learning based\\non GNNs was presented in~\\\\cite{Gar+2017}. Early\\napproaches were published in~\\\\cite{Mer+2005} and~\\\\cite{Sca+2009,\\nSca+2009a}. A survey can be found in~\\\\cite{Ham+2017a}.', '1806.08804v4', 'Related Work'), (24, '13', '}\\n\\\\xhdr{Graph kernels}Graph kernels', '1806.08804v4', 'Related Work'), (25, '14', 'In recent years, various graph kernels have been proposed, which\\nimplicitly or explicitly map graphs to a Hilbert space. Gärtner \\\\etal\\\\cite{Gae+2003}\\nand Kashima \\\\etal~\\\\cite{Kas+2003} simultaneously proposed\\ngraph kernels based on random walks, which count the number of walks\\ntwo graphs have in common. Since then, random walk kernels have been\\nstudied intensively, e.g., see~\\\\cite{Sug+2015}.\\nKernels based on shortest paths were first proposed in~\\\\cite{Borgwardt2005}.', '1806.08804v4', 'Related Work'), (26, '15', 'A different line in the development of graph kernels focused\\nparticularly on scalable graph kernels. These kernels are typically\\ncomputed efficiently by explicit feature maps, which allow to bypass\\nthe computation of a gram matrix, and allow applying scalable linear\\nclassification algorithms. Prominent examples are kernels based on\\nsubgraphs up to a fixed size, so called\\n{graphlets}graphlets~\\\\cite{She+2009}. Other approaches of this category encode\\nthe neighborhood of every node by different techniques, e.g.,\\nsee~\\\\cite{Hid+2009, Neu+2016}, and most notably the\\nWeisfeiler-Lehman subtree kernel~\\\\cite{She+2011} and its higher-order\\nvariants~\\\\cite{Mor+2017}. Subgraph and Weisfeiler-Lehman kernels have\\nbeen successfully employed within frameworks for smoothed and deep\\ngraph kernels~\\\\cite{Yan+2015,Yan+2015a}. Recent developments include\\nassignment-based approaches~\\\\cite{kriege2016valid,Nik+2017,Joh+2015} and\\nspectral approaches~\\\\cite{Kon+2016}. Although graph kernels have shown good performance in the past, they lack the ability to adapt to they give data distribution at hand, since they mostly rely on precomputed features. Moreover, they may not scale to large data sets due to the quadratic overhead to compute the gram matrix.', '1806.08804v4', 'Related Work'), (27, '16', \"Niepert \\\\etal~\\\\cite{niepert2016learning} extracted canonical vector representations of neighborhoods of nodes, based on heuristics such as the Weisfeiler-Lehman algorithm~\\\\cite{She+2011}, to compute a graph embeddings and then used neural networks for the classification task. Moreover, recently graph neural networks (GNN) for node and graph classification became popular. Most of these\\napproaches fit into the framework proposed by~\\\\cite{Gil+2017}. Here a GNN is viewed as a message passing approach where node\\nfeatures are iteratively computed from the features of the\\nnode's neighbors by using a differentialable neighborhood aggregation function. The parameters of this function are \\nlearned together with the parameters of the neural network used for the\\nclassification task in an end-to-end fashion. Up to now, naïve global mean pooling or precomputed clustering methods were applied to compute graph embeddings from the features of each single node.\", '1806.08804v4', 'Related Work'), (28, '17', 'Notable instances of this model include Neural\\nFingerprints~\\\\cite{Duv+2015}, Gated Graph Neural\\nNetworks~\\\\cite{Li+2016}, and spectral approaches proposed\\nin~\\\\cite{Bru+2014,Def+2015,kipf2017semi}. Dai \\\\etal\\\\cite{dai2016discriminative}\\nproposed an approach inspired by mean-field inference\\nand Lei \\\\etal\\\\cite{Lei+2017} showed that the generated\\nfeature maps lie in the same Hilbert space as some popular graph\\nkernels and successfully applied them in the domain of\\nchemoinformatics~\\\\cite{Jin+2018}. In~\\\\cite{simonovsky2017dynamic} GNN were extended to include edge features and various precomputed pooling heuristics based on clustering methods were applied. Attention-based extensions were explored in~\\\\cite{Vel+2018}. In order to make GNNs scale to large graphs Hamilton \\\\etal\\\\cite{hamilton2017inductive} and Chen \\\\etal\\\\cite{Che+2018} devised stochastic versions of GNNs. Recently, a differentiable pooling mechanism to compute graph embeddings based on node features using differentiable sorting was proposed~\\\\cite{zhang2018end}.', '1806.08804v4', 'Related Work'), (29, '18', 'Moreover, GNNs were applied for protein-protein\\ninteraction prediction~\\\\cite{Fou+2017} and quantum interactions in\\nmolecules~\\\\cite{Sch+2017}. An approach for unsupervised learning based\\non GNNs was presented in~\\\\cite{Gar+2017}. Early\\napproaches were published in~\\\\cite{Mer+2005} and~\\\\cite{Sca+2009,\\nSca+2009a}. A survey can be found in~\\\\cite{Ham+2017a}.', '1806.08804v4', 'Related Work'), (30, '0', '\\\\label{sec:proposed}', '1806.08804v4', 'Proposed Method'), (31, '1', 'The key idea of \\\\name is that it enables the construction of deep, multi-layer GNN models by providing a differentiable module to hierarchically pool graph nodes. \\nIn this section, we outline the \\\\name module and show how it is applied in a deep GNN architecture.', '1806.08804v4', 'Proposed Method'), (32, '2', '\\\\subsection{Preliminaries}\\n\\\\label{sec:setting}', '1806.08804v4', 'Proposed Method'), (33, '3', 'We represent a graph $G$G as $(A,F)$(A,F), where $A\\\\in \\\\{0, 1\\\\}^{n\\\\times n}$A\\\\in \\\\{0, 1\\\\}^{n\\\\times n}n\\\\times n is the adjacency matrix, and $F \\\\in \\\\mathbb{R}^{n\\\\times d}$F \\\\in \\\\mathbb{R}^{n\\\\times d}n\\\\times d is the node feature matrix assuming each node has $d$d features.\\\\footnote{We do not consider edge features, although one can easily extend the algorithm to support edge features using techniques introduced in \\\\cite{simonovsky2017dynamic}.}\\nGiven a set of labeled graphs $\\\\mathcal{D}=\\\\{(G_1,y_1),(G_2,y_2),...\\\\}$\\\\mathcal{D}=\\\\{(G_1,y_1),(G_2,y_2),...\\\\} where $y_i \\\\in \\\\mathcal{Y}$y_i \\\\in \\\\mathcal{Y} is the label corresponding to graph $G_i \\\\in \\\\mathcal{G}$G_i \\\\in \\\\mathcal{G}, the goal of graph classification is to learn a mapping $f : \\\\mathcal{G} \\\\to \\\\mathcal{Y}$f : \\\\mathcal{G} \\\\to \\\\mathcal{Y} that maps graphs to the set of labels. \\nThe challenge---compared to standard supervised machine learning setup---is that we need a way to extract useful feature vectors from these input graphs.\\nThat is, in order to apply standard machine learning methods for classification, e.g., neural networks, we need a procedure to convert each graph to an finite dimensional vector in $\\\\mathbb{R}^D$\\\\mathbb{R}^D.', '1806.08804v4', 'Proposed Method'), (111, '1', 'Recurrent neural networks, long short-term memory and gated recurrent neural networks in particular, have been firmly established as state of the art approaches in sequence modeling and transduction problems such as language modeling and machine translation.', '1706.03762v7', 'Introduction'), (35, '5', \"where $H^{(k)} \\\\in \\\\mathbb{R}^{n \\\\times d}$H^{(k)}(k) \\\\in \\\\mathbb{R}^{n \\\\times d}n \\\\times d are the node embeddings (i.e., ``messages'') computed after $k$k steps of the GNN and $M$M is the message propagation function, which depends on the adjacency matrix, trainable parameters $\\\\theta^{(k)}$\\\\theta^{(k)}(k), and the node embeddings $H^{(k-1)}$H^{(k-1)}(k-1) generated from the previous message-passing step.\\\\footnote{For notational convenience, we assume that the embedding dimension is $d$ for all $H^{(k)}$; however, in general this restriction is not necessary.}\\nThe input node embeddings $H^{(0)}$H^{(0)}(0) at the initial message-passing iteration $(k=1)$(k=1), are initialized using\\nthe node features on the graph, $H^{(0)} = F$H^{(0)}(0) = F.\", '1806.08804v4', 'Proposed Method'), (36, '6', \"There are many possible implementations of the propagation function $M$M \\\\cite{Gil+2017,hamilton2017inductive}.\\nFor example, one popular variant of GNNs---Kipf's \\\\etal \\\\cite{kipf2017semi} Graph Convolutional Networks (GCNs)---implements $M$M using a combination of linear transformations and ReLU non-linearities:\\n\\\\begin{equation}\\n \\\\label{eq:gcn}\\n H^{(k)} = M(A,H^{(k-1)};W^{(k)}) = \\\\textrm{ReLU}(\\\\tilde{D}^{-\\\\frac{1}{2}}\\\\tilde{A}\\\\tilde{D}^{-\\\\frac{1}{2}} H^{(k-1)} W^{(k-1)}),\\n\\\\end{equation}\\\\begin{equation}\\n \\\\label{eq:gcn}\\n H^{(k)} = M(A,H^{(k-1)};W^{(k)}) = \\\\textrm{ReLU}(\\\\tilde{D}^{-\\\\frac{1}{2}}\\\\tilde{A}\\\\tilde{D}^{-\\\\frac{1}{2}} H^{(k-1)} W^{(k-1)}),\\n\\\\end{equation}\\n \\\\label{eq:gcn}\\n H^{(k)}(k) = M(A,H^{(k-1)}(k-1);W^{(k)}(k)) = \\\\textrm{ReLU}(\\\\tilde{D}^{-\\\\frac{1}{2}}-\\\\frac{1}{2}\\\\tilde{A}\\\\tilde{D}^{-\\\\frac{1}{2}}-\\\\frac{1}{2} H^{(k-1)}(k-1) W^{(k-1)}(k-1)),\", '1806.08804v4', 'Proposed Method'), (37, '7', 'where $\\\\tilde{A}=A+I$\\\\tilde{A}=A+I, $\\\\tilde{D}=\\\\sum_j\\\\tilde{A}_{ij}$\\\\tilde{D}=\\\\sum_j\\\\tilde{A}_{ij}ij and $W^{(k)} \\\\in \\\\mathbb{R}^{d \\\\times d}$W^{(k)}(k) \\\\in \\\\mathbb{R}^{d \\\\times d}d \\\\times d is a trainable weight matrix.\\nThe differentiable pooling model we propose can be applied to any GNN model implementing Equation \\\\eqref{eq:gnn}, and is agnostic with regards to the specifics of how $M$M is implemented.', '1806.08804v4', 'Proposed Method'), (38, '8', 'A full GNN module will run $K$K iterations of Equation \\\\eqref{eq:gnn} to generate the final output node embeddings $Z=H^{(K)} \\\\in \\\\mathbb{R}^{n \\\\times d}$Z=H^{(K)}(K) \\\\in \\\\mathbb{R}^{n \\\\times d}n \\\\times d, where $K$K is usually in the range 2-6.\\nFor simplicity, in the following sections we will abstract away the internal structure of the GNNs and use $Z = \\\\gnn(A, X)$Z = \\\\gnn(A, X) to denote an arbitrary GNN module implementing $K$K iterations of message passing according to some adjacency matrix $A$A and initial input node features $X$X.', '1806.08804v4', 'Proposed Method'), (39, '9', \"\\\\xhdr{Stacking GNNs and pooling layers}Stacking GNNs and pooling layers\\nGNNs implementing Equation \\\\eqref{eq:gnn} are inherently flat, as they only propagate information across edges of a graph.\\nThe goal of this work is to define a general, end-to-end differentiable strategy that allows one to {\\\\em stack}\\\\em stack multiple GNN modules in a hierarchical fashion. \\nFormally, given $Z = \\\\gnn(A, X)$Z = \\\\gnn(A, X), the output of a GNN module, and a graph adjacency matrix $A \\\\in \\\\mathbb{R}^{n \\\\times n}$A \\\\in \\\\mathbb{R}^{n \\\\times n}n \\\\times n, we seek to define a strategy to output a new coarsened graph containing $m