SciGraphs is an open-source extension that embeds scientific graph processing directly within the modern 3D graphics environment of Blender. It turns abstract networks and real-world spatial graphs into native Blender objects driven by Geometry Nodes, mapping analytical results (centralities, communities, topological invariants) to named attributes that procedurally drive color, scale, and animation — combining the analytical power of NetworkX, igraph and Graphviz with Blender's Cycles & EEVEE path-traced rendering.
SciGraphs supports two complementary domains:
- Combinatorial graphs, where classical network metrics, community structure, and algorithmic layouts are computed and mapped procedurally via attribute-driven properties.
- Spatial and geometric graphs, where networks are visualized using their intrinsic coordinate data (geospatial, sparse-matrix, mesh) or laid out via topological analysis, including multi-layer urban morphologies through the City2Graph module.
By unifying network analysis and visualization within Blender, SciGraphs enables reproducible 3D visualizations that extend network-geometry communication beyond the constraints of traditional 2D canvases (Gephi, Cytoscape, Graphia...).
Granada, Spain. A 37,814-intersection street network plus a 20-NN amenity proximity graph (city2graph) draped onto SRTM 30 m terrain.
- Download the platform zip above for your system
- Open Blender > Edit > Preferences > Add-ons
- Select "Install from Disk", then choose the downloaded zip
- Enable the SciGraphs add-on in case it's not active
To install the latest development version:
- Go to GitHub Actions: https://github.com/SciBlend/SciGraphs/actions
- Select the latest successful workflow run
- Download the artifact for your platform
- Extract the downloaded zip to get the actual extension zip file
- Install manually following the steps above
git clone https://github.com/SciBlend/SciGraphs.git
cd SciGraphs
./scripts/fetch_wheels.sh # download bundled dependency wheels for all platforms
./build_extension.sh # build per-platform zips and install for your OSThe bundled dependency wheels are not tracked in git, so scripts/fetch_wheels.sh must be run first to populate the wheels/ directory (it downloads the Python 3.13 wheels for every platform from the constraints/ files). Then build_extension.sh uses Blender's extension build --split-platforms command to produce per-platform zips in dist/ and installs the one matching your OS.
After enabling, open View3D > Sidebar > SciGraphs.
- Data Import: GEXF, SuiteSparse Matrix Collection (
.mtx), CSV edge lists and node-attribute tables, and live SQL databases (PostgreSQL, MySQL/MariaDB, SQLite; SQL Server optional). Supports temporal graph sequences with playback controls. - OSMnx Street Networks: download by place, address, bounding box, point + radius, polygon or local
.osmXML; routing and shortest paths, accessibility, edge speeds & travel times, centrality, bearings and orientation (rose diagrams), plus elevation/terrain (DEM) and basemap texturing. - City2Graph (Urban Morphology & Transport): Overture Maps import (buildings, places, segments, connectors, land, water), morphological graphs (tessellation + private/public relations), proximity graphs, and GTFS transit analysis (DuckDB-backed) with travel-summary and origin–destination graphs and metapaths.
- Layouts: force-directed (NetworkX spring, igraph Fruchterman–Reingold, Kamada–Kawai, DrL, LGL, Davidson–Harel, Graphopt, ForceAtlas2, and Yifan Hu via Graphviz), spectral (graph-Laplacian eigenvectors), classical MDS, Circle Packing for planar graphs (Collins–Stephenson), geometric 3D distributions (Fibonacci sphere, helix, spiral, cube), and directed/hierarchical layouts (Sugiyama layered, circular hierarchy). Graphviz engines (dot, neato, fdp, sfdp, twopi, circo, osage, patchwork) run via the bundled
scigraphs-utilsbindings. A Network Splitter decomposes layouts into Z-layers by community, degree, centrality or component. - Analysis: centrality for undirected (degree, betweenness, closeness, eigenvector) and directed graphs (PageRank, HITS hub/authority, in/out-degree, Katz); community detection with seven SurpriseMe algorithms (CPM, Infomap, RB, RN, RNSC, SCluster, UVCluster) and the Surprise quality metric via the bundled
pysurprisebindings; directed-structure detection (DAG/tree/forest, SCC/WCC, cycles, sources/sinks/bottlenecks), BFS/DFS traversal and flow animation; and topological analysis (Boyer–Myrvold planarity, Euler characteristic, Kuratowski subgraphs, genus bound). - Visualization: Geometry-Nodes rendering of nodes (instanced primitives) and edges (tubes/curves), attribute-driven coloring with scientific colormaps, attribute-driven node/edge sizing, edge-style presets (Gephi, Cytoscape, Schematic, Bundled, Flow, Minimal), interactive gizmos/toolbar and text-overlay labels.
- Reproducible Pipelines: declarative JSON/YAML workflow specifications with deterministic seeding and provenance manifests.
- Export: GEXF, GraphML, JSON, CSV edge lists, Pajek
.net, node positions and statistics reports, plus a conversion script for PyTorch Geometric structures.
- Blender 5.1.0+
- Bundled Python wheels target Python 3.13 (the interpreter shipped with Blender 5.1.x)
- Platforms: Linux x64, Windows x64, macOS ARM64 (Apple Silicon)
- Internet access is required for geospatial features (geocoding, OSM networks, maps, DEM) and SQL database connections
All third-party dependencies are bundled as wheels — no manual pip install is needed. Key dependencies include networkx, igraph, rustworkx, osmnx, city2graph, overturemaps, geopandas, shapely, pyproj, momepy, libpysal, scikit-learn, scipy, pandas, pyarrow, duckdb, pillow and requests.
SciGraphs integrates several open and commercial data services:
| Source | Used for | Access |
|---|---|---|
| OpenStreetMap / Nominatim | Street networks and geocoding (via OSMnx & geopy) | Public |
| Overture Maps | Buildings, places, segments, connectors, land, water | Public (cloud parquet) |
| OpenTopography / OpenTopoData | Digital Elevation Models (SRTM, etc.) | API key / public |
| Open-Elevation | Point elevation queries | Public |
| OSM / ESRI / Mapbox tiles | Basemap texturing of terrain | Public / token |
| NASA Blue Marble | Globe satellite imagery for geospatial scenes | Public |
| Natural Earth | Land/ocean vector context layers | Public |
| SuiteSparse Matrix Collection | Benchmark sparse-matrix graphs | Public |
| GTFS feeds | Public-transport networks | User-provided |
The native bindings that power the Graphviz layouts (scigraphs-utils) and the Surprise community-detection metric (pysurprise) are built and maintained by the project author, José Marín.
SciGraphs can replay an entire pipeline — dataset, analysis, layout, visualization, render and export — from a short declarative JSON or YAML file:
{
"meta": { "title": "complex_demo", "seed": 42 },
"dataset": { "source": "osmnx", "method": "PLACE", "query": "Burjassot, Valencia, Spain", "network_type": "drive" },
"analysis": { "metrics": ["degree", "betweenness"] },
"layout": { "algorithm": "YIFAN_HU", "scale": 8.0 },
"visual": { "node_color": "betweenness", "node_size": "degree", "colormap": "magma", "edge_style": "GEPHI_DEFAULT" }
}Each run emits a canonical specification, a provenance manifest (with input/output hashes and timing) and an execution log, so the same seed and inputs reproduce the same result. See examples/pipelines/ for ready-to-run pipelines and the full schema reference.
The pipeline transforms raw data into high-fidelity 3D visualizations. Data-ingestion modules normalize inputs from diverse sources (flat files, SQL databases, geospatial data) into a unified internal representation. The processing core bifurcates into two analytical domains — combinatorial (layouts and network metrics) and spatial/geometric (geometry-driven embeddings and geospatial morphology) — and Blender's Geometry Nodes and Cycles engine convert these analytical structures into procedural geometry and photorealistic renders.
- Import or Generate a graph: load a file/database, download an OSMnx network, build an urban morphology graph, or pull a SuiteSparse matrix.
- Analyze: compute centrality, community structure, statistics and topology; results are stored as mesh attributes on the graph object.
- Lay Out: arrange nodes with any of the 2D/3D, force-directed or Graphviz layout algorithms.
- Visualize: set up Geometry Nodes, then drive node/edge color and size from attributes using scientific colormaps and edge-style presets.
- Render & Export: render with Cycles or EEVEE, and export the graph (GEXF, GraphML, JSON, CSV, Pajek), positions, or statistics reports.
 |
 |
| Global migration flows (2000–2016). UNHCR displacement data (188 country nodes, 3,614 weighted edges) projected onto a 3D globe with geodesic arcs and NASA imagery, rendered in Cycles. | SuiteSparse A 3D finite-element-mesh graph (5,425 nodes / 5,872 edges) from the SuiteSparse Matrix Collection, laid out with Yifan Hu (SFDP) and colored by betweenness centrality (Inferno colormap), rendered in Cycles with depth-of-field and adaptive labels. |
SciGraphs is described in:
Visualization and Analysis of Graphs with SciGraphs. José Marín, Ignacio Marín, Ignacio García-Fernández. The Eurographics Association, 2026.
If SciGraphs is used in research or publications, please cite it:
@article{marin2026visualization,
title={Visualization and Analysis of Graphs with SciGraphs},
author={Mar{\'\i}n, Jos{\'e} and Mar{\'\i}n, Ignacio and Garc{\'\i}a-Fern{\'a}ndez, Ignacio},
year={2026},
publisher={The Eurographics Association}
}
(Source code: https://github.com/SciBlend/SciGraphs)
Contributions to SciGraphs are welcome, including issue reporting, feature suggestions and pull requests. Please use the issue tracker in this repository.
For inquiries or support:
- Maintainer: José Marín —
jose.marin-farina@uv.es - Issues: https://github.com/SciBlend/SciGraphs/issues
SciGraphs is released under the GNU General Public License v3.0 or later (GPL-3.0-or-later).