Publications

Conferences and Journals

1. State Space Models on Temporal Graphs: A First-Principles Study
   Jintang Li#, Ruofan Wu#, Xinzhou Jin, Boqun Ma, Liang Chen, Zibin Zheng.
   In Proceedings of the Thirty-eighth Annual Conference on Neural Information Processing (NeurIPS 2024).
   [pdf] [code]

2. FairAGG: Toward Fair Graph Neural Networks via Fair Aggregation
   Yuchang Zhu, Jintang Li, Liang Chen*, Zibin Zheng.
   IEEE Transactions on Computational Social Systems (TCSS 2024).
   [pdf]

3. Revisiting Modularity Maximization for Graph Clustering: A Contrastive Learning Perspective
   Yunfei Liu#, Jintang Li#*, Yuehe Chen, Ruofan Wu, Baokun Wang, Jing Zhou, Sheng Tian, Shuheng Shen, Xing Fu, Changhua Meng, Weiqiang Wang, Liang Chen.
   In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2024).
   [pdf] [code]

4. One Fits All: Learning Fair Graph Neural Networks for Various Sensitive Attributes
   Yuchang Zhu, Jintang Li, Yatao Bian, Zibin Zheng, Liang Chen*.
   In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2024).
   [pdf] [code]

5. Topology-monitorable Contrastive Learning on Dynamic Graphs
   Zulun Zhu, Kai Wang, Haoyu Liu, Jintang Li, Siqiang Luo*.
   In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2024).
   [pdf] [code]

6. Fair Graph Representation Learning via Sensitive Attribute Disentanglement
   Yuchang Zhu, Jintang Li, Zibin Zheng, Liang Chen*.
   In Proceedings of the 16th international conference on World Wide Web (WWW or TheWebConf 2024).
   [pdf] [code]

7. A Graph is Worth 1-bit Spikes: When Graph Contrastive Learning Meets Spiking Neural Networks
   Jintang Li, Huizhe Zhang, Ruofan Wu, Zulun Zhu, Baokun Wang, Changhua Meng, Zibin Zheng, Liang Chen*.
   In Proceedings of the 12th International Conference on Learning Representations (ICLR 2024).
   [pdf] [code]

8. The Devil is in the Data: Learning Fair Graph Neural Networks via Partial Knowledge Distillation
   Yuchang Zhu, Jintang Li, Liang Chen*, Zibin Zheng.
   In Proceedings of the 17th ACM International Conference Web Search and Data Mining (WSDM 2024).
   [pdf] [code]

9. Rethinking and Simplifying Bootstrapped Graph Latents
   Wangbin Sun, Jintang Li, Liang Chen*, Bingzhe Wu, Yatao Bian, Zibin Zheng.
   In Proceedings of the 17th ACM International Conference Web Search and Data Mining (WSDM 2024).
   [pdf] [code]

10. Enhancing Graph Collaborative Filtering via Neighborhood Structure Embedding
    Xinzhou Jin, Jintang Li, Yuanzhen Xie, Liang Chen*, Beibei Kong, Lei Cheng, Bo Hu, Zang Li, Zibin Zheng.
    In Proceedings of the 23rd IEEE International Conference on Data Mining (ICDM 2023).
    [pdf] [code]

11. GUARD: Graph Universal Adversarial Defense
    Jintang Li, Jie Liao, Ruofan Wu, Liang Chen*, Jiawang Dan, Changhua Meng, Zibin Zheng, Weiqiang Wang.
    In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (CIKM 2023).
    [pdf] [code]

12. SAILOR: Structural Augmentation Based Tail Node Representation Learning
    Jie Liao, Jintang Li, Liang Chen*, Bingzhe Wu, Yatao Bian, Zibin Zheng.
    In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (CIKM 2023).
    [pdf] [code]

13. What's Behind the Mask: Understanding Masked Graph Modeling for Graph Autoencoders
    Jintang Li#, Ruofan Wu#, Wangbin Sun, Liang Chen*, Sheng Tian, Liang Zhu, Changhua Meng, Zibin Zheng, Weiqiang Wang.
    In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2023).
    [pdf] [code]

14. SAD: Semi-Supervised Anomaly Detection on Dynamic Graphs
    Sheng Tian, Jihai Dong, Jintang Li, Wenlong Zhao, Xiaolong Xu, Baokun wang, Bowen Song, Changhua Meng, Tianyi Zhang, Liang Chen.
    In Proceedings of 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023).
    [pdf] [code]

15. Scaling Up Dynamic Graph Representation Learning via Spiking Neural Networks
    Jintang Li, Zhouxin Yu, Zulun Zhu, Liang Chen*, Qi Yu, Zibin Zheng, Sheng Tian, Ruofan Wu, Changhua Meng
    In Proceedings of the 36th AAAI Conference on Artificial Intelligence (AAAI 2023).
    [pdf] [code]

16. Spectral Adversarial Training for Robust Graph Neural Network
    Jintang Li, Jiaying Peng, Liang Chen*, Zibin Zheng, Tingting Liang, Qing Ling.
    IEEE Transactions on Knowledge and Data Engineering (TKDE 2022).
    [pdf] [code]

17. Unifying multi-associations through hypergraph for bundle recommendation
    Zhouxin Yu, Jintang Li, Liang Chen*, Zibin Zheng.
    Knowledge-Based Systems (KBS 2022).
    [pdf] [code]

18. Spiking Graph Convolutional Networks
    Zulun Zhu, Jiaying Peng, Jintang Li, Liang Chen*, Qi Yu, Siqiang Luo.
    In Proceedings of 31th International Joint Conference on Artificial Intelligence (IJCAI 2022).
    [pdf] [code]

19. Graph Enhanced Neural Interaction Model for recommendation
    Liang Chen, Tao Xie, Jintang Li, Zibin Zheng*.
    Knowledge-Based Systems (KBS 2022).
    [pdf]

20. Understanding Structural Vulnerability in Graph Convolutional Networks
    Liang Chen, Jintang Li, Qibiao Peng, Yang Liu, Zibin Zheng*, Carl Yang.
    In Proceedings of 30th International Joint Conference on Artificial Intelligence (IJCAI 2021).
    [pdf] [code]

21. Adversarial Attack on Large Scale Graph
    Jintang Li, Tao Xie, Liang Chen*, Fenfang Xie, Xiangnan He, Zibin Zheng.
    IEEE Transactions on Knowledge and Data Engineering (TKDE 2021).
    [pdf] [code]

22. GraphGallery: A Platform for Fast Benchmarking and Easy Development of Graph Neural Networks Based Intelligent Software
    Jintang Li, Kun Xu, Liang Chen*, Zibin Zheng and Xiao Liu.
    In Proceedings of 43rd International Conference on Software Engineering (ICSE 2021).
    [pdf] [code]

23. Phishing Scams Detection in Ethereum Transaction Network,
    Liang Chen, Jiaying Peng, Yang Liu, Jintang Li, Fenfang Xie, Zibin Zheng.
    ACM Transactions on Internet Technology (TOIT 2021).
    [pdf]

24. Deep Insights into Graph Adversarial Learning: An Empirical Study Perspective
    Jintang Li, Zishan Gu, Qibiao Peng, Kun Xu, Liang Chen*, and Zibin Zheng.
    Joint Workshop on Human Brain and Artificial Intelligence, in Conjunction With IJCAI-PRICAI. (IJCAI-HBAI 2021).

Preprints

1. Revisiting and Benchmarking Graph Autoencoders: A Contrastive Learning Perspective
   Jintang Li, Ruofan Wu, Yuchang Zhu, Huizhe Zhang, Xinzhou Jin, Guibin Zhang, Zulun Zhu, Zibin Zheng, Liang Chen*.
   arXiv, 2024.
   [pdf] [code]

2. LasTGL: An Industrial Framework for Large-Scale Temporal Graph Learning
   Jintang Li, Jiawang Dan, Ruofan Wu, Jing Zhou, Sheng Tian, Yunfei Liu, Baokun Wang*, Changhua Meng, Weiqiang Wang, Yuchang Zhu, Liang Chen*, Zibin Zheng.
   arXiv, 2023.
   [pdf]

3. Hetero$^2$Net: Heterophily-aware Representation Learning on Heterogenerous Graphs
   Jintang Li, Zheng Wei, Jiawang Dan, Jing Zhou, Yuchang Zhu, Ruofan Wu, Baokun Wang, Zhang Zhen, Changhua Meng, Hong Jin, Zibin Zheng, Liang Chen*.
   arXiv, 2023.
   [pdf] [code]

4. Oversmoothing: A Nightmare for Graph Contrastive Learning?
   Jintang Li, Wangbin Sun, Ruofan Wu, Yuchang Zhu, Zibin Zheng, Liang Chen*.
   arXiv, 2023.
   [pdf] [code]

5. Less Can Be More: Unsupervised Graph Pruning for Large-scale Dynamic Graphs
   Jintang Li#, Sheng Tian#, Ruofan Wu, Liang Zhu, Wenlong Zhao, Changhua Meng, Liang Chen*, Zibin Zheng, Hongzhi Yin.
   arXiv, 2023.
   [pdf] [code]

6. A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection
   Bingzhe Wu, Jintang Li, Junchi Yu, Yatao Bian, Hengtong Zhang, CHaochao Chen, Chengbin Hou, Guoji Fu, Liang Chen*, Tingyang Xu, Yu Rong, Xiaolin Zheng, Junzhou Huang, Ran He, Baoyuan Wu, GUangyu Sun, Peng Cui, Zibin Zheng, Zhe Liu, Peilin Zhao.
   arXiv, 2022.
   [pdf]

7. Recent Advances in Reliable Deep Graph Learning: Inherent Noise, Distribution Shift, and Adversarial Attack
   Jintang Li, Bingzhe Wu*, Chengbin Hou, Guoji Fu, Yatao Bian, Liang Chen, Junzhou Huang.
   arXiv, 2022.
   [pdf]

8. Neighboring Backdoor Attacks on Graph Convolutional Network
   Liang Chen*, Qibiao Peng, Jintang Li, Yang Liu, Jiawei Chen, Yong Li, Zibin Zheng.
   arXiv, 2022.
   [pdf] [code]

9. A Survey of Adversarial Learning on Graphs
   Liang Chen*, Jintang Li, Jiaying Peng, Tao Xie, Zengxu Cao, Kun Xu, Xiangnan He, Zibin Zheng.
   arXiv, 2020.
   [pdf] [paper list]