Toward Interpretable and Stable Graph Neural Networks

Description

Graphs are ubiquitous data structures in numerous domains, such as social science (social networks), natural science (physical systems, and protein-protein interaction networks) and knowledge graphs. As new generalizations of traditional deep neural networks to graph structured data, Graph Neural Networks (or GNNs) have demonstrated the power in graph representation learning and have permeated numerous areas of science and technology. However, GNNs also inherited the drawback of traditional deep neural networks, i.e., lacking interpretability. Moreover, graph neural networks are vulnerable to adversarial attacks. These drawbacks have raised tremendous concerns to adopt GNNs in many critical applications pertaining. Thus, this project aims to tackle the major drawbacks of GNNs and greatly enlarge their usability in critical applications. To achieve the research goal, this project systematically investigates advanced principles for new mechanisms to interpret GNNs, understand their vulnerabilities and develop robust GNNs. The proposed research extends the state-of-the-art GNNs to a new frontier, investigates original problems that entreat innovative solutions and paves the way for a new research endeavor effectively tame graph mining.

Publications

• Haochen Liu, Yiqi Wang, Wenqi Fan, Xiaorui Liu, Yaxin Li, Shaili Jain, Yunhao Liu, Anil K. Jain and Jiliang Tang. ``Trustworthy AI: A Computational Perspective '', In ACM Transactions on Intelligent Systems and Technology (TIST-22)
• Enyan Dai, Tianxiang Zhao, Huaisheng Zhu, Junjie Xu, Zhimeng Guo, Hui Liu, Jiliang Tang, and Suhang Wang. ``A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability '', arXiv preprint arXiv:2204.08570 (Arxiv-22)
• Hongzhi Wen, Jiayuan Ding, Wei Jin, Yuying Xie, Jiliang Tang. ``Graph Neural Networks for Multimodal Single-Cell Data Integration'', In Proceedings of 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD-22)
• Wei Jin, Xiaorui Liu, Yao Ma, Charu Aggarwal, Jiliang Tang. ``Feature Overcorrelation in Deep Graph Neural Networks: A New Perspective'', In Proceedings of 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD-22)
• Wei Jin, Xianfeng Tang, Haoming Jiang, Zheng Li, Danqing Zhang, Jiliang Tang, Ying Bin. ``Condensing Graphs via One-Step Gradient Matching'', IIn Proceedings of 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD-22)
• Wei Jin, Lingxiao Zhao, Shichang Zhang, Yozen Liu, Jiliang Tang and Neil Shah. ``Graph Condensation for Graph Neural Networks'', In Proceedings of International Conference on Learning Representations (ICLR-22)
• Yao Ma, Xiaorui Liu, Neil Shah and Jiliang Tang. ``Is Homophily a Necessity for Graph Neural Networks'', In Proceedings of International Conference on Learning Representations (ICLR-22)
• Teng Xiao, Zhengyu Chen, and Suhang Wang. ``Representation Matters When Learning From Biased Feedback in Recommendation'', In Proceedings of 31st ACM International Conference on Information and Knowledge Management (CIKM-22)
• Wenqi Fan, Xiaorui Liu, Wei Jin, Xiangyu Zhao, Jiliang Tang, Qing Li. ``Graph Trend Filtering Networks for Recommendation'', In Proceedings of 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR-22)
• Tianxiang Zhao, Xiang Zhang, and Suhang Wang. ``Exploring Edge Disentanglement for Node Classification'', In Proceedings of the Web Conference (WWW-22)
• Teng Xiao, and Suhang Wang. ``Towards Off-Policy Learning for Ranking Policies with Logged Feedback'', In Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI-22)
• Enyan Dai, Jin Wei, Hui Liu, and Suhang Wang. ``Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labels'', In Proceedings of the 15th ACM International Conference on Web Search and Data Mining (WSDM-22)
• Teng Xiao, and Suhang Wang. ``Towards Unbiased and Robust Causal Ranking for Recommender Systems'', In Proceedings of the 15th ACM International Conference on Web Search and Data Mining (WSDM-22)
• Xianfeng Tang, Yozen Liu, Xinran He, Suhang Wang, and Neil Shah. ``Ranking Friend Stories on Social Platforms with Edge-Contextual Local Graph Convolutions'', In Proceedings of the 15th ACM International Conference on Web Search and Data Mining (WSDM-22)
• Yao Ma, Suhang Wang, Tyler Derr, Lingfei Wu and Jiliang Tang. ``Graph Adversarial Attack via Rewiring'', In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD-21)
• Enyan Dai, Charu Aggarwal, and Suhang Wang. ``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 and Data Mining (KDD-21)
• Teng Xiao, Zhengyu Chen, Donglin Wang and Suhang Wang. ``Learning How to Propagate Messages in Graph Neural Networks'', In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD-21)
• Yao Ma, Xiaorui Liu, Tong Zhao, Yozen Liu, Jiliang Tang and Neil Shah. ``A Unified View on Graph Neural Networks as Graph Signal Denoising'', In Proceedings of the 2021 ACM on Conference on Information and Knowledge Management (CIKM-21)
• Enyan Dai, and Suhang Wang. ``Towards Self-Explaianble Graph Neural Networks'', In Proceedings of 30th ACM International Conference on Information and Knowledge Management (CIKM-21)
• Xiaorui Liu, Jiayuan Ding, Wei Jin, Han Xu, Yao Ma, Zitao Liu and Jiliang Tang. ``Graph Neural Networks with Adaptive Residual'', Residual In Proceedings of the 35th Conference on Neural Information Processing Systems (Neurips-21)

Resources

• scMoGNN (Graph Neural Networks for Multimodal Single-Cell Data Integration)
• Decorr (Feature Overcorrelation in Deep Graph Neural Networks: A New Perspective)
• DosCond (Condensing Graphs via One-Step Gradient Matching)
• GCond (Graph Condensation for Graph Neural Networks)
• GTN (GTN: Graph Trend Filtering Networks for Recommendations)
• DisGNN (Exploring Edge Disentanglement for Node Classification)
• RS-GNN (Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labels)
• ReWatt (Graph Adversarial Attack via Rewiring)
• NRGNN (NRGNN: Learning a Label Noise Resistant Graph Neural Network on Sparsely and Noisily Labeled Graphs)
• Ada-UGNN (A Unified View on Graph Neural Networks as Graph Signal Denoising)
• SE-GNN (Towards Self-Explaianble Graph Neural Networks)
• AirGNN (Graph Neural Networks with Adaptive Residual)

Project Members

• Suhang Wang (PI)
• Jiliang Tang (PI)
• Zhimeng Guo (PhD Student)
• Huaisheng Zhu (PhD Student)
• Shijie Zhou (PhD Student)
• Xiaorui Liu (PhD Student)
• Jamell Decon (PhD Student)
• Yaxin Li (PhD Student)
• Wei Jin (PhD Student)
• Juanhui Li (PhD Student)

Acknowledgments

Created by Suhang Wang who can be reached at szw494 at psu.edu.
Webmaster: Huaisheng Zhu, Email: hvz5312 at psu.edu.

Last Upadted: August 31, 2022