Jintang Li's Homepage

Jintang Li (李金膛 in Chinese) Ph.D Student Curriculum Vitae (CV) School of Software Engineering Sun Yat-sen University (SYSU) PyG Team E-mail: lijt55 [AT] mail2.sysu.edu.cn [GitHub] [Google Scholar] [DBLP] [Zhihu]

About me

I am currently a Ph.D student at Sun Yat-sen University, where I am advised by Prof. Liang Chen. I received the master's degree from Sun Yat-sen University in 2021.

My research interests include:

• Trustworthy Graph Learning: reliability, fairness, etc.

• Graph Self-supervised Learning

• Graph Neural Networks

• Spiking Neural Networks

• Large Language Models

🔥🔥🔥 I am in the 2025 fall job market and actively seeking postdoctoral and industry opportunities. Feel free to reach out to me via email or WeChat (id: EdisonLeejt).

PyTorch Geometric

As a core maintainer of PyTorch Geometric (PyG), I can provide assistance with any issues you may encounter while using PyG. Feel free to reach out to me for support or guidance in working with PyG.

Educations

Sun Yat-sen University Ph.D in Software Engineering, from August 2021 to June 2025 (Expected). Sun Yat-sen University M.S. in Electronics and Communications Engineering, from August 2019 to June 2021.

Experiences

[2022-2023] Research Intern at Ant Group, from February 2022 to June 2022. [2023-2024] Research Intern at Ant Group, from July 2023 to March 2024.

Recent news

• [arXiv 2024] October 15, 2024: Check out our new preprint: graph autoencoders benchmark.

• [NeurIPS 2024] October 12, 2024: I received the NeurIPS 2024 Scholar Award.

• [NeurIPS 2024] September 26, 2024: GraphSSM has been accepted to NeurIPS 2024.

• [WWW 2025] August 18, 2024: I was intived as a Graph reviewer for WWW 2025.

• [ICLR 2025] August 13, 2024: I was intived as a reviewer for ICLR 2025.

• [arXiv 2024] June 04, 2024: Check out our new preprint: state space models on temporal graphs.

• [NeurIPS 2024] May 22, 2024: I was intived as a reviewer for NeurIPS 2024.

• [KDD 2024] May 17, 2024: Three papers on (i) graph contrastive learning and (ii) fair graph learning have been accepted by KDD 2024!

• [KDD 2024] February 10, 2024: I was intived as a reviewer for KDD 2024.

• [WWW 2024] January 23, 2024: Our work on fair graph learning has been accepted by WWW (TheWebConf) 2024!

• [ICLR 2024] January 16, 2024: Our work on binary graph contrastive learning has been accepted by ICLR 2024 (poster)!

• [SYSU 2023] December 14, 2023, I was nominated with the Academic Star Award in SYSU (逸仙学术之星提名奖, Top 0.02%).

• [WWW 2024 ] October 24, 2023: I was invited as a reviewer for WWW 2024.

• [WSDM 2024] October 20, 2023: Two papers accepted to WSDM 2024!

• [arXiv 2023] October 19, 2023: Check out our new preprint on Heterophilic Heterogeneous graphs.

• [LoG 2023] September 3, 2023: I was invited as the reviewer for LoG 2023.

• [ICDM 2023] September 3, 2023: One paper has been accepted by ICDM 2023!

• [CIKM 2023] August 5, 2023: Two papers on (i) robust graph learning and (ii) long-tail graph learning have been accepted by CIKM 2023!

• [KDD 2023] May 18, 2023: One paper on understanding masked graph autoencoders has been accepted by KDD 2023!

• [IJCAI 2023] April 20, 2023: One paper on semi-supervised anomaly detection has been accepted by IJCAI 2023!

• [AAAI 2023] November 19, 2022: One paper on spiking graph learning has been accepted by AAAI 2023 Oral!

• [TKDE 2022] November 4, 2022: One paper on robust graph neural networks has been accepted by TKDE!

• [PyG Team] September 23, 2022. I've joined the PyG Team!

• [KDD 2022] June 11, 2022: We gave a tutorial on Trustworthy Graph Learning with our collaborators.

• [IJCAI 2022] April 21, 2022: One paper on spiking graph convolutional networks has been accepted for a Long Oral presentation.

Selected Publications

Note: * for corresponding author, # for equal contribution.
Please find my full list of publications in the following Link.

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. 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]

3. 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]

4. 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]

5. 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]

6. 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]

7. 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]

8. 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]

9. 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]

10. 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]

11. 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]

12. 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]

13. 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]

14. 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]

15. 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]

16. 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]

17. 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]

18. 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]

19. 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]

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. 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]

Projects

1. PyTorch Geometric (collaborator): Graph Neural Network Library for PyTorch.

2. GraphGallery: A gallery for benchmarking Graph Neural Networks (GNNs).

3. GreatX: A graph reliability toolbox based on PyTorch and PyTorch Geometric.

4. Mooon: A graph data augmentation library based on PyTorch and PyTorch Geometric.

5. Awesome Graph Adversarial Learning: A curated collection of adversarial attack and defense on graph data.

6. Awesome Fair Graph Learning: Paper Lists for Fair Graph Learning (FairGL).

7. Awesome Masked Autoencoders: A collection of literature after or concurrent with Masked Autoencoder (MAE).

Rewards

I am interested in participating in AI competitions and would love to collaborate with others! Please feel free to reach out to me if you are also interested in such collaborations.

1. The 2nd World AI4S Prize, LLM Logical Reasoning track - 🏆1st place.

2. Car Drag Detection Challenge, IJCAI 2024 [Link], track 1 & 3 - 🥉3rd place.

3. Deepfake Speech Detection Challenge, IJCAI 2024 [Link], 🥈2nd place.

4. Medical Treatment and Public Health, Seed 2023 [Link], 🏆1st place.

5. Ant Group ATEC 2023 [Link], online track 2 & 3 - 🏆1st place, final - 🥈2nd place.

6. iFLYTEK AI development competition 2023: Social account network classification [Link], 🏆1st place.

7. iFLYTEK AI development competition 2023: New user prediction [Link], 🥈2nd place.

8. Ant Group ATEC 2022 [Link], 🥈2nd place.

9. Baidu AI Competition 2023: CVR Prediction. [Link], 🥈2nd place.

10. CAAI-BDSC 2023, Dynamic Link Prediction In Social Knowledge Graphs. [Link], 🥉3rd place.

11. Ant Group Green Computing Contest. [Link], 🥈2nd place.

12. CIKM 2022 AnalytiCup Competition: Federated Hetero-Task Learning. [Link] [Code], 🏅4th place.

13. ICDM 2022 Competition: Risk Commodities Detection on Large-Scale E-Commence Graphs. [Link] [Code], 🥉3rd place.

14. FinvCup 2022: Fraud User Risk Identification. [Link], [Code], 🏅9th place.

15. Ant Group ATEC 2021: truthworthy AI. [Link], 🥈2nd place.

16. Ant Group ATEC 2021 online, Track 2: Fraud detection of digital currency transactions. [Link], 🏅4th place.

17. Spectra Review Paper Competition 2022 (Spring) 🏆winner. [Link].

18. Spectra Review Paper Competition 2021. [Link], 🥉3rd place winner with [Introduction on Graph Adversarial Learning].

19. KDD Cup 2020, Adversarial Attacks and Defense on Academic Graph. [Link], 🥈2nd place.

Talks

1. KDD 2022 tutorial: Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection.

2. AI TIME IJCAI 2021: Understanding Structural Vulnerability in Graph Convolutional Networks (in Chinese).

Scholarship & Honors

1. National Scholarship: 2022 & 2023 at Sun Yat-sen University (Top 0.2%).

2. The Academic Star Nomination Award at Sun Yat-sen University (逸仙学术之星提名奖, Top 0.02%). [News]

3. Top-100 students at Sun Yat-sen University (百年校庆百名优秀学子). [News]

Professional services

• Reviewer: AAAI, IJCAI, WWW, KDD, LoG, ICLR, TKDD, JMLR, TPAMI etc.

Useful Links

Deadlines: ccf-ddl
CCF list: ccf.atom.im