Awesome Robust Graph Learning Resources

Robust graph learning is to develop learning algorithms that maintain predictive accuracy and stability in the presence of structural noise, adversarial perturbations, and out-of-distribution (OOD) shifts.

This repository collects:

1. Academic Papers
2. Online Courses and Videos
3. Graph Datasets
4. Open-source and Commercial Libraries/Toolkits
5. Key Conferences & Journals

More items will be added to the repository. Please feel free to suggest other key resources by opening an issue report, submitting a pull request, or dropping me an email @ (liuyang173@mails.ucas.edu.cn). Enjoy reading!

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Table of Contents

• 1. Tutorials & Benchmarks
  • 1.1. Benchmarks
  • 1.2. Tutorials
• 2. Toolbox & Datasets
  • 2.1. Toolboxes & Libraries
  • 2.2. Datasets
• 3. Papers
  • 3.1. Graph Adversarial Attack
  • 3.2. Graph Adversarial Defense & Robust GNNs
  • 3.3. Graph Structure Learning & Learning under Noisy/Weak Information
  • 3.4. Graph Anomaly Detection
  • 3.5. Graph Out-of-Distribution Generalization
• 4. Key Conferences/Workshops/Journals
  • 4.1. Conferences & Workshops
  • 4.2. Journals

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1. Tutorials & Benchmarks

1.1. Benchmarks

Scope	Paper Title	Venue	Year	Ref	Materials
Adversarial Robustness	Graph Robustness Benchmark: Benchmarking the Adversarial Robustness of Graph Machine Learning	NeurIPS	2021	[1]	[PDF], [Code], [Home]
OOD Generalization	GOOD: A Graph Out-of-Distribution Benchmark	NeurIPS	2022	[2]	[PDF], [Code]
Anomaly Detection	GADBench: Revisiting and Benchmarking Supervised Graph Anomaly Detection	NeurIPS	2023	[3]	[PDF], [Code]
Anomaly Detection	BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs	NeurIPS	2022	[4]	[PDF], [Code]
Structure Learning	OpenGSL: A Comprehensive Benchmark for Graph Structure Learning	NeurIPS	2023	[5]	[PDF], [Code]
Large-scale Graph ML	Open Graph Benchmark: Datasets for Machine Learning on Graphs	NeurIPS	2020	[6]	[PDF], [Code]
Adaptive-attack Audit	Are Defenses for Graph Neural Networks Robust?	NeurIPS	2022	[7]	[PDF], [Code]

Note

GRB, GOOD, GADBench, BOND and OpenGSL are NeurIPS Datasets and Benchmarks Track papers; Are Defenses for GNNs Robust? is an adaptive-attack re-evaluation showing many published defenses give little gain over an undefended baseline.

1.2. Tutorials

Tutorial Title	Venue	Year	Ref	Materials
Robust Graph Learning in Finance	ICAIF	2025	[8]	[HTML], [ICAIF]
Robust Graph Learning on the Web: Challenges, Methods, and Applications	WWW	2026	[9]	[HTML]

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2. Toolbox & Datasets

2.1. Toolboxes & Libraries

Toolbox	Description	Materials
DeepRobust	PyTorch library of adversarial attacks and defenses (image + graph); de-facto standard for graph adversarial robustness.	[Code], [PDF]
GRB	Modular, reproducible framework for benchmarking the adversarial robustness of graph ML.	[Code], [Home]
GreatX	Toolbox for graph reliability and adversarial robustness (formerly GraphWar), built on PyG.	[Code]
GUARD	Graph universal adversarial defense; training-free defensive patch for GCNs (CIKM 2023).	[Code], [PDF]
PyGOD	Comprehensive Python library for graph outlier / anomaly detection (JMLR 2024).	[Code], [PDF]
PyTorch Geometric	Widely used GNN library; foundation many robust-graph toolboxes build on.	[Code]
DGL	Framework-agnostic, high-performance package for deep learning on graphs.	[Code]
CogDL	Comprehensive graph deep-learning toolkit; also hosts GRB (WWW 2023).	[Code]
DIG	Turnkey research library: graph generation, self-supervised learning, explainability, and OOD.	[Code]
GraphGym	Platform for GNN design-space exploration and reproducible experiments (now integrated into PyG).	[Code]

2.2. Datasets

Category	Datasets	Typical Use	Materials
Citation networks	Cora, CiteSeer, PubMed	Standard node-classification benchmark for attacks/defenses and GSL	[Planetoid], [PyG]
Co-purchase / Co-author	Amazon Computers/Photo, Coauthor CS/Physics	Robustness to data splits; semi-supervised node classification	[Repo]
Political blogs	Polblogs	Featureless graph for structure attacks (Nettack / Metattack)	[Data]
Large-scale	ogbn-arxiv, ogbn-products	Scalable robustness and OOD evaluation	[OGB]
Inductive	Reddit, Flickr	Inductive node classification and scalability	[GraphSAGE], [GraphSAINT]
Fraud / Anomaly	YelpChi, Amazon (fraud)	Multi-relation fraud / graph anomaly detection	[CARE-GNN]
Financial anomaly	T-Finance, T-Social, Elliptic	Large-scale anomaly / anti-money-laundering detection	[BWGNN], [Elliptic]
OOD splits	GOOD-Cora, GOOD-CBAS	Covariate / concept shift evaluation	[GOOD]
Molecular OOD	DrugOOD	Molecular property OOD (scaffold / size / assay splits)	[DrugOOD]

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3. Papers

3.1. Graph Adversarial Attack

Category	Paper Title	Venue	Year	Ref	Materials
Evasion Attack	Adversarial Attack on Graph Structured Data	ICML	2018	[10]	[PDF], [Code]
Evasion Attack	Adversarial Attacks on Neural Networks for Graph Data	KDD	2018	[11]	[PDF], [Code]
Evasion Attack	Indirect Adversarial Attacks via Poisoning Neighbors for Graph Convolutional Networks	BigData	2019	[13]	[PDF], Code: N/A
Evasion / Poisoning	Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective	IJCAI	2019	[12]	[PDF], [Code]
Evasion (Black-box)	A Restricted Black-box Adversarial Framework Towards Attacking Graph Embedding Models	AAAI	2020	[14]	[PDF], [Code]
Evasion (Universal)	Graph Universal Adversarial Attacks: A Few Bad Actors Ruin Graph Learning Models	IJCAI	2021	[15]	[PDF], [Code]
Poisoning Attack	Adversarial Attacks on Graph Neural Networks via Meta Learning	ICLR	2019	[16]	[PDF], [Code]
Injection Attack	Adversarial Attacks on Graph Neural Networks via Node Injections: A Hierarchical RL Approach	WWW	2020	[17]	[Paper], Code: N/A
Injection Attack	TDGIA: Effective Injection Attacks on Graph Neural Networks	KDD	2021	[18]	[PDF], [Code]
Injection Attack	Single Node Injection Attack against Graph Neural Networks	CIKM	2021	[19]	[PDF], [Code]
Injection Attack	Understanding and Improving Graph Injection Attack by Promoting Unnoticeability	ICLR	2022	[20]	[PDF], [Code]
Injection (Query-based)	Cluster Attack: Query-based Adversarial Attacks on Graphs with Graph-Dependent Priors	IJCAI	2022	[21]	[PDF], [Code]
Backdoor Attack	Graph Backdoor	USENIX Security	2021	[22]	[PDF], [Code]
Backdoor Attack	Backdoor Attacks to Graph Neural Networks	SACMAT	2021	[23]	[PDF], [Code]
Backdoor Attack	Unnoticeable Backdoor Attacks on Graph Neural Networks	WWW	2023	[24]	[PDF], [Code]
Backdoor Attack	SPEAR: A Structure-Preserving Manipulation Method for Graph Backdoor Attacks	WWW	2025	[25]	[PDF], [Code]

3.2. Graph Adversarial Defense & Robust GNNs

Model- and structure-level defenses against adversarial perturbations: robust aggregation, robust spectral filtering, preprocessing/purification, adversarial training, and certified robustness.

Category	Paper Title	Venue	Year	Ref	Materials
Preprocessing	Adversarial Examples for Graph Data: Deep Insights into Attack and Defense	IJCAI	2019	[26]	[PDF]
Robust Architecture	Robust Graph Convolutional Networks Against Adversarial Attacks	KDD	2019	[27]	[Paper], [Code]
Certified Defense	Certifiable Robustness and Robust Training for Graph Convolutional Networks	KDD	2019	[28]	[PDF], [Code]
Certified Defense	Certifiable Robustness to Graph Perturbations	NeurIPS	2019	[29]	[PDF], [Code]
Preprocessing (Low-rank)	All You Need Is Low (Rank): Defending Against Adversarial Attacks on Graphs	WSDM	2020	[30]	[PDF]
Certified Defense	Efficient Robustness Certificates for Discrete Data: Sparsity-Aware Randomized Smoothing	ICML	2020	[31]	[PDF], [Code]
Structure Learning	Graph Structure Learning for Robust Graph Neural Networks	KDD	2020	[32]	[PDF], [Code]
Robust Aggregation	GNNGuard: Defending Graph Neural Networks against Adversarial Attacks	NeurIPS	2020	[33]	[PDF], [Code]
Robust Aggregation	Reliable Graph Neural Networks via Robust Aggregation	NeurIPS	2020	[34]	[PDF], [Code]
Robust Aggregation	Node Similarity Preserving Graph Convolutional Networks	WSDM	2021	[35]	[PDF], [Code]
Robust Message Passing	Elastic Graph Neural Networks	ICML	2021	[36]	[PDF], [Code]
Robust Aggregation (Scalable)	Robustness of Graph Neural Networks at Scale	NeurIPS	2021	[37]	[PDF], [Code]
Structure Refinement	Reliable Representations Make A Stronger Defender: Unsupervised Structure Refinement for Robust GNN	KDD	2022	[38]	[PDF]
Robust Spectral Filter	EvenNet: Ignoring Odd-Hop Neighbors Improves Robustness of Graph Neural Networks	NeurIPS	2022	[39]	[PDF], [Code]
Robust Spectral Filter	Robust Mid-Pass Filtering Graph Convolutional Networks	WWW	2023	[40]	[PDF]
Adversarial Defense	A Simple and Yet Fairly Effective Defense for Graph Neural Networks	AAAI	2024	[41]	[PDF], [Code]
Diffusion Purification	Robust Graph Learning Against Adversarial Evasion Attacks via Prior-Free Diffusion-Based Structure Purification	WWW	2025	[42]	[PDF]

3.3. Graph Structure Learning & Learning under Noisy/Weak Information

Methods that learn, refine, or denoise the graph structure, and that learn under noisy labels, missing attributes, or otherwise weak/incomplete information.

Category	Paper Title	Venue	Year	Ref	Materials
Structure Learning	Learning Discrete Structures for Graph Neural Networks	ICML	2019	[43]	[PDF], [Code]
Structure Learning	Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings	NeurIPS	2020	[44]	[PDF], [Code]
Label-Noise Robustness	NRGNN: Learning a Label Noise-Resistant GNN on Sparsely and Noisily Labeled Graphs	KDD	2021	[45]	[PDF], [Code]
Unsupervised GSL	Towards Unsupervised Deep Graph Structure Learning	WWW	2022	[46]	[PDF], [Code]
Structure Learning (MI)	Compact Graph Structure Learning via Mutual Information Compression	WWW	2022	[47]	[PDF], [Code]
Attribute Imputation	Initializing Then Refining: A Simple Graph Attribute Imputation Network	IJCAI	2022	[48]	[PDF]
Label-Noise Robustness	Robust Training of Graph Neural Networks via Noise Governance	WSDM	2023	[49]	[PDF], [Code]
Weak Information	Learning Strong Graph Neural Networks with Weak Information	KDD	2023	[50]	[PDF], [Code]
Robust Structure Refinement	Self-Guided Robust Graph Structure Refinement	WWW	2024	[51]	[PDF], [Code]
Noisy Structure + Missing Attr.	Dilution of Unreliable Information: Learning in Graph with Noisy Structures and Absent Attributes	ICDM	2025	[52]	[PDF]

3.4. Graph Anomaly Detection

Category	Paper Title	Venue	Year	Ref	Materials
Reconstruction	Deep Anomaly Detection on Attributed Networks	SDM	2019	[53]	[Paper]
Fraud Detection	Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters	CIKM	2020	[54]	[PDF], [Code]
Contrastive (Unsupervised)	Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning	IEEE TNNLS	2022	[55]	[PDF]
One-class	One-class Graph Neural Networks for Anomaly Detection in Attributed Networks	Neural Comput. Appl.	2021	[56]	[PDF], [Code]
Fraud (Imbalance)	Pick and Choose: A GNN-based Imbalanced Learning Approach for Fraud Detection	WWW	2021	[57]	[PDF], [Code]
Contrastive (Multi-scale)	ANEMONE: Graph Anomaly Detection with Multi-Scale Contrastive Learning	CIKM	2021	[58]	[Paper], [Code]
Generative + Contrastive	Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection	IEEE TKDE	2021	[59]	[PDF], [Code]
Spectral (Supervised)	Rethinking Graph Neural Networks for Anomaly Detection	ICML	2022	[60]	[PDF], [Code]
Heterophily-aware	Addressing Heterophily in Graph Anomaly Detection: A Perspective of Graph Spectrum	WWW	2023	[61]	[PDF], [Code]
One-class Homophily	Truncated Affinity Maximization: One-class Homophily Modeling for Graph Anomaly Detection	NeurIPS	2023	[63]	[PDF], [Code]
Graph-level	Graph-level Anomaly Detection via Hierarchical Memory Networks	ECML PKDD	2023	[62]	[PDF], [Code]
Reconstruction	GAD-NR: Graph Anomaly Detection via Neighborhood Reconstruction	WSDM	2024	[64]	[PDF], [Code]
Semi-supervised (Generative)	Generative Semi-supervised Graph Anomaly Detection	NeurIPS	2024	[65]	[PDF], [Code]

3.5. Graph Out-of-Distribution Generalization

Methods for generalizing GNNs under distribution shift: invariant / causal learning, size shift, environment-based augmentation, test-time adaptation, and surveys.

Category	Paper Title	Venue	Year	Ref	Materials
Size Shift	From Local Structures to Size Generalization in Graph Neural Networks	ICML	2021	[66]	[PDF]
Shift-Robust Training	Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training Data	NeurIPS	2021	[67]	[PDF], [Code]
Invariant Learning	Handling Distribution Shifts on Graphs: An Invariance Perspective	ICLR	2022	[68]	[PDF], [Code]
Invariant Learning	Discovering Invariant Rationales for Graph Neural Networks	ICLR	2022	[69]	[PDF], [Code]
Invariant / Causal Learning	Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs	NeurIPS	2022	[70]	[PDF], [Code]
Augmentation (Rationale)	Graph Rationalization with Environment-based Augmentations	KDD	2022	[71]	[PDF], [Code]
Environment Inference	Learning Substructure Invariance for Out-of-Distribution Molecular Representations	NeurIPS	2022	[72]	[OpenReview], [Code]
Size Shift	SizeShiftReg: A Regularization Method for Improving Size-Generalization in GNNs	NeurIPS	2022	[73]	[PDF], [Code]
Test-Time Adaptation	Empowering Graph Representation Learning with Test-Time Graph Transformation	ICLR	2023	[74]	[PDF], [Code]
Causal Stable Learning	Generalizing Graph Neural Networks on Out-of-Distribution Graphs	IEEE TPAMI	2024	[75]	[PDF]
Causal Independence	Joint Learning of Label and Environment Causal Independence for Graph OOD Generalization	NeurIPS	2023	[76]	[PDF], [Code]
Invariant Learning (Theory)	Does Invariant Graph Learning via Environment Augmentation Learn Invariance?	NeurIPS	2023	[77]	[PDF], [Code]
Causal Intervention	Graph Out-of-Distribution Generalization via Causal Intervention	WWW	2024	[78]	[PDF], [Code]

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4. Key Conferences/Workshops/Journals

4.1. Conferences & Workshops

ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD).

ACM International Conference on Management of Data (SIGMOD)

The Web Conference (WWW)

Conference on Neural Information Processing Systems (NeurIPS)

International Conference on Machine Learning (ICML)

International Conference on Learning Representations (ICLR)

IEEE International Conference on Data Mining (ICDM)

SIAM International Conference on Data Mining (SDM)

IEEE International Conference on Data Engineering (ICDE)

ACM InternationalConference on Information and Knowledge Management (CIKM)

ACM International Conference on Web Search and Data Mining (WSDM)

The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD)

The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)

USENIX Security Symposium

4.2. Journals

ACM Transactions on Knowledge Discovery from Data (TKDD)

IEEE Transactions on Knowledge and Data Engineering (TKDE)

IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)

IEEE Transactions on Neural Networks and Learning Systems (TNNLS)

ACM SIGKDD Explorations Newsletter

Data Mining and Knowledge Discovery

Knowledge and Information Systems (KAIS)

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References

[1]	Qinkai Zheng, Xu Zou, Yuxiao Dong, Yukuo Cen, Da Yin, Jiarong Xu, Yang Yang, and Jie Tang. 2021. Graph Robustness Benchmark: Benchmarking the Adversarial Robustness of Graph Machine Learning. In Advances in Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track.

[2]	Shurui Gui, Xiner Li, Limei Wang, and Shuiwang Ji. 2022. GOOD: A Graph Out-of-Distribution Benchmark. In Advances in Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track.

[3]	Jianheng Tang, Fengrui Hua, Ziqi Gao, Peilin Zhao, and Jia Li. 2023. GADBench: Revisiting and Benchmarking Supervised Graph Anomaly Detection. In Advances in Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track.

[4]

Kay Liu, Yingtong Dou, Yue Zhao, Xueying Ding, Xiyang Hu, Ruitong Zhang, Kaize Ding, Canyu Chen, Hao Peng, Kai Shu, Lichao Sun, Jundong Li, George H. Chen, Zhihao Jia, and Philip S. Yu. 2022. BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs. In Advances in Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track.

[5]	Zhiyao Zhou, Sheng Zhou, Bochao Mao, Xuanyi Zhou, Jiawei Chen, Qiaoyu Tan, Daochen Zha, Yan Feng, Chun Chen, and Can Wang. 2023. OpenGSL: A Comprehensive Benchmark for Graph Structure Learning. In Advances in Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track.

[6]	Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec. 2020. Open Graph Benchmark: Datasets for Machine Learning on Graphs. In Advances in Neural Information Processing Systems (NeurIPS).

[7]	Felix Mujkanovic, Simon Geisler, Stephan Gunnemann, and Aleksandar Bojchevski. 2022. Are Defenses for Graph Neural Networks Robust? In Advances in Neural Information Processing Systems (NeurIPS).

[8]	Xiang Ao, Yang Liu, Guansong Pang, Yuanhao Ding, Hezhe Qiao, Dawei Cheng, and Qing He. 2025. Robust Graph Learning in Finance. In Proceedings of the 6th ACM International Conference on AI in Finance (ICAIF).

[9]	Xiang Ao, Yang Liu, Guansong Pang, Yuanhao Ding, Hezhe Qiao, Dawei Cheng, and Qing He. 2026. Robust Graph Learning on the Web: Challenges, Methods, and Applications. In Companion Proceedings of the ACM Web Conference (WWW).

[10]	Hanjun Dai, Hui Li, Tian Tian, Xin Huang, Lin Wang, Jun Zhu, and Le Song. 2018. Adversarial Attack on Graph Structured Data. In Proceedings of the 35th International Conference on Machine Learning (ICML), 1115-1124.

[11]	Daniel Zugner, Amir Akbarnejad, and Stephan Gunnemann. 2018. Adversarial Attacks on Neural Networks for Graph Data. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD), 2847-2856. https://doi.org/10.1145/3219819.3220078.

[12]	Kaidi Xu, Hongge Chen, Sijia Liu, Pin-Yu Chen, Tsui-Wei Weng, Mingyi Hong, and Xue Lin. 2019. Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective. In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI), 3961-3967.

[13]	Tsubasa Takahashi. 2019. Indirect Adversarial Attacks via Poisoning Neighbors for Graph Convolutional Networks. In 2019 IEEE International Conference on Big Data (Big Data), 1395-1400.

[14]	Heng Chang, Yu Rong, Tingyang Xu, Wenbing Huang, Honglei Zhang, Peng Cui, Wenwu Zhu, and Junzhou Huang. 2020. A Restricted Black-box Adversarial Framework Towards Attacking Graph Embedding Models. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 3389-3396.

[15]	Xiao Zang, Yi Xie, Jie Chen, and Bo Yuan. 2021. Graph Universal Adversarial Attacks: A Few Bad Actors Ruin Graph Learning Models. In Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI), 3328-3334.

[16]	Daniel Zugner and Stephan Gunnemann. 2019. Adversarial Attacks on Graph Neural Networks via Meta Learning. In International Conference on Learning Representations (ICLR).

[17]	Yiwei Sun, Suhang Wang, Xianfeng Tang, Tsung-Yu Hsieh, and Vasant Honavar. 2020. Adversarial Attacks on Graph Neural Networks via Node Injections: A Hierarchical Reinforcement Learning Approach. In Proceedings of The Web Conference 2020 (WWW), 673-683. https://doi.org/10.1145/3366423.3380149.

[18]

Xu Zou, Qinkai Zheng, Yuxiao Dong, Xinyu Guan, Evgeny Kharlamov, Jialiang Lu, and Jie Tang. 2021. TDGIA: Effective Injection Attacks on Graph Neural Networks. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2461-2471. https://doi.org/10.1145/3447548.3467314.

[19]	Shuchang Tao, Qi Cao, Huawei Shen, Junjie Huang, Yunfan Wu, and Xueqi Cheng. 2021. Single Node Injection Attack against Graph Neural Networks. In Proceedings of the 30th ACM International Conference on Information and Knowledge Management (CIKM), 1794-1803.

[20]	Yongqiang Chen, Han Yang, Yonggang Zhang, Kaili Ma, Tongliang Liu, Bo Han, and James Cheng. 2022. Understanding and Improving Graph Injection Attack by Promoting Unnoticeability. In International Conference on Learning Representations (ICLR).

[21]	Zhengyi Wang, Zhongkai Hao, Ziqiao Wang, Hang Su, and Jun Zhu. 2022. Cluster Attack: Query-based Adversarial Attacks on Graphs with Graph-Dependent Priors. In Proceedings of the 31st International Joint Conference on Artificial Intelligence (IJCAI), 768-775.

[22]	Zhaohan Xi, Ren Pang, Shouling Ji, and Ting Wang. 2021. Graph Backdoor. In 30th USENIX Security Symposium (USENIX Security 21), 1523-1540.

[23]	Zaixi Zhang, Jinyuan Jia, Binghui Wang, and Neil Zhenqiang Gong. 2021. Backdoor Attacks to Graph Neural Networks. In Proceedings of the 26th ACM Symposium on Access Control Models and Technologies (SACMAT), 15-26. https://doi.org/10.1145/3450569.3463560.

[24]	Enyan Dai, Minhua Lin, Xiang Zhang, and Suhang Wang. 2023. Unnoticeable Backdoor Attacks on Graph Neural Networks. In Proceedings of the ACM Web Conference 2023 (WWW), 2263-2273. https://doi.org/10.1145/3543507.3583392.

[25]	Yuanhao Ding, Yang Liu, Yugang Ji, Weigao Wen, Qing He, and Xiang Ao. 2025. SPEAR: A Structure-Preserving Manipulation Method for Graph Backdoor Attacks. In Proceedings of the ACM Web Conference 2025 (WWW), 1237-1247. https://doi.org/10.1145/3696410.3714665.

[26]

Huijun Wu, Chen Wang, Yuriy Tyshetskiy, Andrew Docherty, Kai Lu, and Liming Zhu. 2019. Adversarial Examples for Graph Data: Deep Insights into Attack and Defense. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI), 4816-4823. https://doi.org/10.24963/ijcai.2019/669.

[27]	Dingyuan Zhu, Ziwei Zhang, Peng Cui, and Wenwu Zhu. 2019. Robust Graph Convolutional Networks Against Adversarial Attacks. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD), 1399-1407.

[28]	Daniel Zugner and Stephan Gunnemann. 2019. Certifiable Robustness and Robust Training for Graph Convolutional Networks. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD), 246-256.

[29]	Aleksandar Bojchevski and Stephan Gunnemann. 2019. Certifiable Robustness to Graph Perturbations. In Advances in Neural Information Processing Systems (NeurIPS).

[30]	Negin Entezari, Saba A. Al-Sayouri, Amirali Darvishzadeh, and Evangelos E. Papalexakis. 2020. All You Need Is Low (Rank): Defending Against Adversarial Attacks on Graphs. In Proceedings of the 13th ACM International Conference on Web Search and Data Mining (WSDM), 169-177.

[31]	Aleksandar Bojchevski, Johannes Klicpera, and Stephan Gunnemann. 2020. Efficient Robustness Certificates for Discrete Data: Sparsity-Aware Randomized Smoothing for Graphs, Images and More. In Proceedings of the 37th International Conference on Machine Learning (ICML), 1003-1013.

[32]	Wei Jin, Yao Ma, Xiaorui Liu, Xianfeng Tang, Suhang Wang, and Jiliang Tang. 2020. Graph Structure Learning for Robust Graph Neural Networks. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 66-74.

[33]	Xiang Zhang and Marinka Zitnik. 2020. GNNGuard: Defending Graph Neural Networks against Adversarial Attacks. In Advances in Neural Information Processing Systems (NeurIPS).

[34]	Simon Geisler, Daniel Zugner, and Stephan Gunnemann. 2020. Reliable Graph Neural Networks via Robust Aggregation. In Advances in Neural Information Processing Systems (NeurIPS).

[35]	Wei Jin, Tyler Derr, Yiqi Wang, Yao Ma, Zitao Liu, and Jiliang Tang. 2021. Node Similarity Preserving Graph Convolutional Networks. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining (WSDM), 148-156.

[36]	Xiaorui Liu, Wei Jin, Yao Ma, Yaxin Li, Hua Liu, Yiqi Wang, Ming Yan, and Jiliang Tang. 2021. Elastic Graph Neural Networks. In Proceedings of the 38th International Conference on Machine Learning (ICML), 6837-6849.

[37]	Simon Geisler, Tobias Schmidt, Hakan Sirin, Daniel Zugner, Aleksandar Bojchevski, and Stephan Gunnemann. 2021. Robustness of Graph Neural Networks at Scale. In Advances in Neural Information Processing Systems (NeurIPS).

[38]	Kuan Li, Yang Liu, Xiang Ao, Jianfeng Chi, Jinghua Feng, Hao Yang, and Qing He. 2022. Reliable Representations Make A Stronger Defender: Unsupervised Structure Refinement for Robust GNN. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 925-935.

[39]	Runlin Lei, Zhen Wang, Yaliang Li, Bolin Ding, and Zhewei Wei. 2022. EvenNet: Ignoring Odd-Hop Neighbors Improves Robustness of Graph Neural Networks. In Advances in Neural Information Processing Systems (NeurIPS).

[40]	Jincheng Huang, Lun Du, Xu Chen, Qiang Fu, Shi Han, and Dongmei Zhang. 2023. Robust Mid-Pass Filtering Graph Convolutional Networks. In Proceedings of the ACM Web Conference 2023 (WWW), 328-338.

[41]	Sofiane Ennadir, Yassine Abbahaddou, Johannes F. Lutzeyer, Michalis Vazirgiannis, and Henrik Bostrom. 2024. A Simple and Yet Fairly Effective Defense for Graph Neural Networks. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 21063-21071.

[42]	Jiayi Luo, Qingyun Sun, Haonan Yuan, Xingcheng Fu, and Jianxin Li. 2025. Robust Graph Learning Against Adversarial Evasion Attacks via Prior-Free Diffusion-Based Structure Purification. In Proceedings of the ACM Web Conference 2025 (WWW).

[43]	Luca Franceschi, Mathias Niepert, Massimiliano Pontil, and Xiao He. 2019. Learning Discrete Structures for Graph Neural Networks. In Proceedings of the 36th International Conference on Machine Learning (ICML), 1972-1982.

[44]	Yu Chen, Lingfei Wu, and Mohammed J. Zaki. 2020. Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings. In Advances in Neural Information Processing Systems (NeurIPS).

[45]	Enyan Dai, Charu Aggarwal, and Suhang Wang. 2021. NRGNN: Learning a Label Noise Resistant Graph Neural Network on Sparsely and Noisily Labeled Graphs. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (KDD), 227-236.

[46]	Yixin Liu, Yu Zheng, Daokun Zhang, Hongxu Chen, Hao Peng, and Shirui Pan. 2022. Towards Unsupervised Deep Graph Structure Learning. In Proceedings of the ACM Web Conference 2022 (WWW), 1392-1403.

[47]	Nian Liu, Xiao Wang, Lingfei Wu, Yu Chen, Xiaojie Guo, and Chuan Shi. 2022. Compact Graph Structure Learning via Mutual Information Compression. In Proceedings of the ACM Web Conference 2022 (WWW), 1601-1610.

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