Awesome Trustworthy Graph Neural Networks

This repository aims to provide links to works in trustworthy graph neural networks. If you find this repo useful, please cite our survey A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability with:

@article{dai2022comprehensive,
  title={A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability},
  author={Dai, Enyan and Zhao, Tianxiang and Zhu, Huaisheng and Xu, Junjie and Guo, Zhimeng and Liu, Hui and Tang, Jiliang and Wang, Suhang},
  journal={arXiv preprint arXiv:2204.08570},
  year={2022}
}

Content

• 1. Survey Papers
• 2. Datasets
  • 2.1 Fairness
  • 2.2 Privacy
  • 2.3 Explainability
• 3. Fairness
• 4. Privacy
  • 4.1 Privacy Attacks on Graphs
  • 4.2 Privacy-Preserving GNNs
• 5. Explainability
  • 5.1 Self-Explainable GNNs
  • 5.2 Posthoc Explainable GNNs
• 6. Robustness
  • 6.1 Graph Adversarial Attacks
  • 6.2 Robust GNNs

1. Survey Papers

1. Adversarial Attacks and Defenses on Graphs: A Review and Empirical Study. SIGKDD Explorations 2020. [paper] [code]
2. A Survey of Adversarial Learning on Graphs. arxiv, 2020. [paper]
3. Adversarial Attacks and Defenses in Images, Graphs and Text: A Review. arxiv, 2019. [paper]
4. Adversarial Attack and Defense on Graph Data: A Survey. arxiv 2018. [paper]

2. Datasets

2.1 Fairness

Dataset	Task	Labels	Sensitive Attributes	Link
Pokec-n	Node classificaiton	Job	Region	[code]
Pokec-z	Node classificaiton	Job	Region	[code]
NBA	Node classificaiton	Salary	Nationality	[code]
German Credit	Node classificaiton	Credit Risk	Gender	[code]
Recidivism	Node classificaiton	Bail	Race	[code]
Credit Defaulter	Node classificaiton	Default	Age	[code]
MovieLens	Link Prediction	-	Multi-attribute	[code]
Reddit	Link Prediction	-	Multi-attribute	[code]
Polblog	Link Prediction	-	Community	[code]
Twitter	Link Prediction	-	Politics	[code]
Facebook	Link Prediction	-	Gender	[code]
Google+	Link Prediction	-	Gender	[code]
Dutch	Link Prediction	-	Gender	[code]

2.2 Privacy

Dataset	Type	Graphs	Avg. Nodes	Avg. Edges	Features
Coauthor	Authorship	1	34493	247962	8415
ACM	Authorship	1	3025	26256	1870
Facebook	Social Networks	1	4039	88234	-
LastFM	Social Networks	1	7624	27806	7824
Reddit	Social Networks	1	232965	57307946	602
Flickr	Image	1	89250	449878	500
PROTEINS	Bioinformatics	1113	39.06	72.82	29
DD	Bioinformatics	1178	284.32	715.66	89
ENZYMES	Bioinformatics	600	32.63	62.14	21
NCI1	Molecule	4110	29.87	32.30	37
AIDS	Molecule	2000	15.69	16.20	42
OVCAR-8H	Molecule	4052	46.67	48.70	65

2.3 Explainability

Dataset	Task	#Graphs	#Nodes	Link
BA-Shapes	Node classification	1	700	4,110
BA-Community	Node classification	1	1,400	8,920
Tree-Cycles	Node classification	1	871	1,950
Tree-Grid	Node classification	1	1,231	3,410
Syn-Cora	Node classification	1	1,895	2,769
BA-2motifs	Graph classification	1,000	25	51.4
Infection	Graph classification	10	1000	3996
Graph-SST2	Graph classification	70,042	10.199	9.20
Graph-SST5	Graph classification	11,855	19.849	18.849
Graph-Twitter	Graph classification	6,949	21.103	21.10
MUTAG	Graph classification	188	19.79	17.93

3. Fairness

1. EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks WWW 2022. [paper], [code]
2. Unbiased graph embedding with biased graph observations WWW 2022. [paper]
3. CrossWalk: Fairness-enhanced Node Representation Learning AAAI 2022. [paper], [code]
4. Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Information WSDM 2021. [paper], [code]
5. Towards a unified framework for fair and stable graph representation learning UAI 2021. [paper], [code]
6. InFoRM: Individual Fairness on Graph Mining KDD 2020. [paper], [code]
7. FairDrop: Biased Edge Dropout for Enhancing Fairness in Graph Representation Learning IEEE Transactions on Artificial Intelligence 2021. [paper], [code]
8. On dyadic fairness: Exploring and mitigating bias in graph connections ICLR 2021. [paper], [code]
9. Individual fairness for graph neural networks: A ranking based approach KDD 2021. [paper], [code]
10. Fairness-Aware Node Representation Learning KDD 2021. [paper]
11. DeBayes: a Bayesian Method for Debiasing Network Embeddings ICML 2020. [paper], [code]
12. Bursting the filter bubble: Fairness-aware network link prediction AAAI 2020. [paper], [code]
13. Compositional Fairness Constraints for Graph Embeddings ICML 2019. [paper], [code]
14. Fairwalk: Towards fair graph embedding IJCAI 2019. [paper], [code]

4. Privacy

4.1 Privacy Attacks on Graphs

1. Quantifying Privacy Leakage in Graph Embedding. Duddu, Vasisht, Antoine Boutet, and Virat Shejwalkar. MobiQuitous 2020. [paper], [code]
2. Membership Inference Attack on Graph Neural Networks. Olatunji, Iyiola E., Wolfgang Nejdl, and Megha Khosla. TPS-ISA 2021. [paper], [code]
3. Node-Level Membership Inference Attacks Against Graph Neural Networks. Xinlei He, Rui Wen, Yixin Wu, Michael Backes, Yun Shen, Yang Zhang. ArXiv 2021. [paper], [code]
4. Adapting Membership Inference Attacks to GNN for Graph Classification: Approaches and Implications. Bang Wu, Xiangwen Yang, Shirui Pan, Xingliang Yuan. ICDM 2021. [paper], [code]
5. Inference Attacks Against Graph Neural Networks. Zhang, Zhikun and Chen, Min and Backes, Michael and Shen, Yun and Zhang, Yang. USENIX Security 2022. [paper], [code]
6. Stealing Links from Graph Neural Networks. Xinlei He, Jinyuan Jia, Michael Backes, Neil Zhenqiang Gong, Yang Zhang. USENIX Security 2021. [paper], [code]
7. Graphmi: Extracting private graph data from graph neural networks. Zaixi Zhang, Qi Liu, Zhenya Huang, Hao Wang, Chengqiang Lu, Chuanren Liu, Enhong Chen. . [paper], [code]
8. Model extraction attacks on graph neural networks: Taxonomy and realization. Bang Wu, Xiangwen Yang, Shirui Pan, Xingliang Yuan. ASIA CCS 2022,. [paper], [code]
9. Model stealing attacks against inductive graph neural networks. Yun Shen, Xinlei He, Yufei Han, Yang Zhang. IEEE S&P 2022. [paper], [code]

4.2 Privacy-Preserving GNNs

1. Releasing Graph Neural Networks with Differential Privacy. Iyiola E. Olatunji, Thorben Funke, Megha Khosla. ArXiv 2021. [paper], [code]
2. Locally Private Graph Neural Networks. Sajadmanesh, Sina and Gatica-Perez, Daniel. CCS 2021. [paper], [code]
3. DPNE: Differentially Private Network Embedding. Depeng Xu, Shuhan Yuan, Xintao Wu, and HaiNhat Phan. PKDD 2018. [paper], [code]
4. Graph Embedding for Recommendation against Attribute Inference Attacks. Shijie Zhang, Hongzhi Yin, Tong Chen, Zi Huang, Lizhen Cui, Xiangliang Zhang. Web Conf. 2021. [paper], [code]
5. FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation. Chuhan Wu, Fangzhao Wu, Yang Cao, Yongfeng Huang, Xing Xie. ArXiv 2021. [paper], [code]
6. Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification. Chaochao Chen, Jun Zhou, Longfei Zheng, Huiwen Wu, Lingjuan Lyu, Jia Wu, Bingzhe Wu, Ziqi Liu, Li Wang, Xiaolin Zheng. ArXiv 2020. [paper], [code]
7. Federated Social Recommendation with Graph Neural Network. Zhiwei Liu, Liangwei Yang, Ziwei Fan, Hao Peng, Philip S. Yu. TIST 2021. [paper], [code]
8. SpreadGNN: Serverless Multi-task Federated Learning for Graph Neural Networks. Chaoyang He, Emir Ceyani, Keshav Balasubramanian, Murali Annavaram, Salman Avestimehr. ArXiv 2021. [paper], [code]
9. Decentralized Federated Graph Neural Networks. Yang Pei1, Renxin Mao, Yang Liu, Chaoran Chen, Shifeng Xu, Feng Qiang. FTL-IJCAI 2021. [paper], [code]
10. Federated Graph Classification over Non-IID Graphs. Han Xie, Jing Ma, Li Xiong, Carl Yang. NIPS 2021. [paper], [code]
11. GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphs. Binghui Wang, Ang Li, Hai Li, Yiran Chen. ArXiv 2020. [paper], [code]
12. ASFGNN: Automated separated-federated graph neural network. Longfei Zheng, Jun Zhou, Chaochao Chen, Bingzhe Wu, Li Wang, Benyu Zhang. [paper], [code]
13. Adversarial Privacy Preserving Graph Embedding against Inference Attack. Kaiyang Li, Guangchun Luo, Yang Ye, Wei Li, Shihao Ji, Zhipeng Cai. IEEE IoT 2020. [paper], [code]
14. Information Obfuscation of Graph Neural Networks. Peiyuan Liao, Han Zhao, Keyulu Xu, Tommi Jaakkola, Geoffrey Gordon, Stefanie Jegelka, Ruslan Salakhutdinov. ICML 2021. [paper], [code]
15. Privacy-Preserving Representation Learning on Graphs: A Mutual Information Perspective. Binghui Wang, Jiayi Guo, Ang Li, Yiran Chen, Hai Li. KDD 2021. [paper], [code]

5. Explainability

5.1 Self-Explainable GNNs

1. Towards Self-Explainable Graph Neural Network. CIKM 2021. [paper]
2. ProtGNN: Towards Self-Explaining Graph Neural Networks. AAAI 2022. [paper]
3. Interpretable and Generalizable Graph Learning via Stochastic Attention Mechanism. Arxiv 2022. [paper]
4. KerGNNs: Interpretable Graph Neural Networks with Graph Kernels. AAAI 2022. [paper]

5.2 Posthoc Explainable GNNs

1. Gnnexplainer: Generating explanations for graph neural networks. Ying Rex, Bourgeois Dylan, You Jiaxuan, Zitnik Marinka, Leskovec Jure. NeurIPS 2019. [paper] [code]
2. Explainability methods for graph convolutional neural networks. Pope Phillip E, Kolouri Soheil, Rostami Mohammad, Martin Charles E, Hoffmann Heiko. CVPR 2019.[paper]
3. Parameterized Explainer for Graph Neural Network. Luo Dongsheng, Cheng Wei, Xu Dongkuan, Yu Wenchao, Zong Bo, Chen Haifeng, Zhang Xiang. NeurIPS 2020. [paper] [code]
4. Xgnn: Towards model-level explanations of graph neural networks. Yuan Hao, Tang Jiliang, Hu Xia, Ji Shuiwang. KDD 2020. [paper].
5. Evaluating Attribution for Graph Neural Networks. Sanchez-Lengeling Benjamin, Wei Jennifer, Lee Brian, Reif Emily, Wang Peter, Qian Wesley, McCloskey Kevin, Colwell Lucy, Wiltschko Alexander. NeurIPS 2020.[paper]
6. PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networks. Vu Minh, Thai My T.. NeurIPS 2020. [paper]
7. Causal Screening to Interpret Graph Neural Networks. [paper]
8. GraphSVX: Shapley Value Explanations for Graph Neural Networks. ECML PKDD 2021. [paper]
9. GNES: Learning to Explain Graph Neural Networks. ICDM 2021. [paper]
10. Generative Causal Explanations for Graph Neural Networks. ICML 2021. [paper]
11. On Explainability of Graph Neural Networks via Subgraph Explorations. ICML 2021. [paper]
12. Zorro: Valid, Sparse, and Stable Explanations in Graph Neural Networks. [paper]
13. Robust Counterfactual Explanations on Graph Neural Networks. Neurips 2021. [paper]
14. When Comparing to Ground Truth is Wrong: On Evaluating GNN Explanation Methods. KDD 2021. [paper]
15. Towards Multi-Grained Explainability for Graph Neural Networks. Neurips 2021. [paper]
16. Reinforcement Learning Enhanced Explainer for Graph Neural Networks. Neurips 2021. [paper]
17. Discovering Invariant Rationales for Graph Neural Networks. ICLR 2022. [paper]
18. Zorro: Valid, Sparse, and Stable Explanations in Graph Neural Networks. Arxiv 2021. [paper]
19. On Consistency in Graph Neural Network Interpretation. Arxiv 2022. [paper]
20. GRAPHSHAP: Motif-based Explanations for Black-box Graph Classifiers. Arxiv 2022. [paper]
21. MotifExplainer: a Motif-based Graph Neural Network Explainer. Arxiv 2022. [paper]
22. Reinforced Causal Explainer for Graph Neural Networks. TPAMI 2022. [paper]
23. CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks. AISTATS 2022 [paper]
24. Prototype-Based Explanations for Graph Neural Networks. AAAI 2022. [paper]
25. FlowX: Towards Explainable Graph Neural Networks via Message Flows. OpenReview 2021. [paper]

6. Robustness

6.1 Graph Adversarial Attacks

1. Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective. Kaidi Xu, Hongge Chen, Sijia Liu, Pin-Yu Chen, Tsui-Wei Weng, Mingyi Hong, Xue Lin. IJCAI 2019. [paper] [code]
2. Fast Gradient Attack on Network Embedding. Jinyin Chen, Yangyang Wu, Xuanheng Xu, Yixian Chen, Haibin Zheng, Qi Xuan. arxiv 2018. [paper] [code]
3. Adversarial Examples on Graph Data: Deep Insights into Attack and Defense. Huijun Wu, Chen Wang, Yuriy Tyshetskiy, Andrew Docherty, Kai Lu, Liming Zhu. IJCAI 2019. [paper] [code]
4. Robustness of Graph Neural Networks at Scale. NeurIPS 2021. [paper] [code]
5. Adversarial Attack on Large Scale Graph. TKDE 2021. [paper]
6. Scalable Attack on Graph Data by Injecting Vicious Nodes. arxiv 2020. [paper]
7. Graph Backdoor. Zhaohan Xi, Ren Pang, Shouling Ji, Ting Wang. USENIX 2021. [paper]
8. Backdoor Attacks to Graph Neural Networks. Zaixi Zhang, Jinyuan Jia, Binghui Wang, Neil Zhenqiang Gong. arxiv 2020. paper
9. A Restricted Black-box Adversarial Framework Towards Attacking Graph Embedding Models. Heng Chang, Yu Rong, Tingyang Xu, Wenbing Huang, Honglei Zhang, Peng Cui, Wenwu Zhu, Junzhou Huang. AAAI 2020. [paper] [code]
10. Adversarial Attacks on Node Embeddings via Graph Poisoning. Aleksandar Bojchevski, Stephan Günnemann. ICML 2019. [paper] [code]
11. Adversarial Attack on Graph Structured Data. [paper] [code]
12. Adversarial Attacks on Neural Networks for Graph Data. Daniel Zügner, Amir Akbarnejad, Stephan Günnemann. KDD 2018. [paper] [code]
13. Attacking Graph Neural Networks at Scale. Simon Geisler, Daniel Zügner, Aleksandar Bojchevski, Stephan Günnemann. AAAI workshop 2021. [paper]
14. Attacking Graph-based Classification via Manipulating the Graph Structure. Binghui Wang, Neil Zhenqiang Gong. CCS 2019. [paper]
15. Adversarial Attacks on Graph Neural Networks via Meta Learning. Daniel Zugner, Stephan Gunnemann. ICLR 2019. [paper] [code]
16. Adversarial attacks on neural networks for graph data KDD 2018. [paper] [code]
17. Attacking Graph Convolutional Networks via Rewiring. Yao Ma, Suhang Wang, Lingfei Wu, Jiliang Tang. arxiv 2019. [paper]
18. Adversarial attacks on graph neural networks via node injections: A hierarchical reinforcement learning approach. WWW 2020 [paper]
19. Towards More Practical Adversarial Attacks on Graph Neural Networks. Jiaqi Ma, Shuangrui Ding, Qiaozhu Mei. NeurIPS 2020. [paper] [code]
20. Single Node Injection Attack against Graph Neural Networks CIKM 2021. [[Paper]] [[code]]
21. Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labels arxiv 2022. [[Paper]] [[code]]

6.2 Robust GNNs

1. Adversarial training methods for network embedding. WWW 2019. [paper] [code]
2. Robustness of Graph Neural Networks at Scale. NeurIPS 2021. [paper] [code]
3. Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective. Kaidi Xu, Hongge Chen, Sijia Liu, Pin-Yu Chen, Tsui-Wei Weng, Mingyi Hong, Xue Lin. IJCAI 2019. [paper] [code]
4. Adversarial Attack on Graph Structured Data. [paper] [code]
5. Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure Fuli Feng, Xiangnan He, Jie Tang, Tat-Seng Chua. TKDE 2019. [paper]
6. GraphDefense: Towards Robust Graph Convolutional Networks. Xiaoyun Wang, Xuanqing Liu, Cho-Jui Hsieh. arxiv 2019. [paper]
7. All You Need is Low (Rank): Defending Against Adversarial Attacks on Graphs. Negin Entezari, Saba Al-Sayouri, Amirali Darvishzadeh, and Evangelos E. Papalexakis. WSDM 2020. [paper] [code]
8. GNNGuard: Defending Graph Neural Networks against Adversarial Attacks. NeurIPS 2020. [paper]
9. Node Similarity Preserving Graph Convolutional Networks. WSDM 2021. [paper] [code]
10. Robust Graph Neural Network Against Poisoning Attacks via Transfer Learning. Xianfeng Tang, Yandong Li, Yiwei Sun, Huaxiu Yao, Prasenjit Mitra, Suhang Wang. WSDM 2020. [paper]
11. Robust Graph Convolutional Networks Against Adversarial Attacks. Dingyuan Zhu, Ziwei Zhang, Peng Cui, Wenwu Zhu. KDD 2019. [paper]
12. Adversarial Examples on Graph Data: Deep Insights into Attack and Defense. Huijun Wu, Chen Wang, Yuriy Tyshetskiy, Andrew Docherty, Kai Lu, Liming Zhu. IJCAI 2019. [paper] [code]
13. Learning to drop: Robust graph neural network via topological denoising. WSDM 2021. [[paper]] [[code]]
14. Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labels. WSDM 2022. [[paper]] [[code]]
15. Graph Structure Learning for Robust Graph Neural Networks. Wei Jin, Yao Ma, Xiaorui Liu, Xianfeng Tang, Suhang Wang, Jiliang Tang. KDD 2020. [paper] [code]
16. Can Adversarial Network Attack be Defended? arxiv 2019. [[paper]] [[code]]
17. Learning robust representations with graph denoising policy network. ICDM 2019. [[paper]] [[code]]
18. Batch Virtual Adversarial Training for Graph Convolutional Networks. Zhijie Deng, Yinpeng Dong, Jun Zhu. ICML 2019 Workshop. [paper]
19. Understanding structural vulnerability in graph convolutional networks arxiv 2021. [[paper]] [[code]]
20. Towards Self-Explainable Graph Neural Network. CIKM 2021. [[paper]] [[code]]
21. Graph Contrastive Learning with Augmentations. NeurIPS 2020. [paper] [code]
22. Robust Unsupervised Graph Representation Learning via Mutual Information Maximization arxiv 2022. [[paper]]
23. Certified Robustness of Graph Convolution Networks for Graph Classification under Topological Attacks. NeurIPS 2020. [paper] [code]
24. Adversarial Immunization for Improving Certifiable Robustness on Graphs. Arxiv 2020. [paper]
25. Certified Robustness of Graph Neural Networks against Adversarial Structural Perturbation. Arxiv 2020. [paper]
26. Efficient Robustness Certificates for Graph Neural Networks via Sparsity-Aware Randomized Smoothing. ICML 2020. [paper] [code]
27. Certifiable Robustness of Graph Convolutional Networks under Structure Perturbations. KDD 2020. [paper] [code]
28. Certified Robustness of Community Detection against Adversarial Structural Perturbation via Randomized Smoothing. Jinyuan Jia, Binghui Wang, Xiaoyu Cao, Neil Zhenqiang Gong. WWW 2020. [paper]
29. Certifiable Robustness to Graph Perturbations. Aleksandar Bojchevski, Stephan Günnemann. NeurIPS 2019. [paper][code]
30. Certifiable Robustness and Robust Training for Graph Convolutional Networks. Daniel Zügner Stephan Günnemann. KDD 2019. [paper] [code]