Jintang Li's Homepage

Jintang Li (李金膛) Ph.D Student School of Software Engineering Sun Yat-sen University (SYSU) PyG Team E-mail: lijt55@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 Neural Networks

• Spiking Neural Networks

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

• Research Intern in Ant Group, from February 2022 to June 2022.

Recent news

• [KDD 2023] May 18, 2023: our work on Understanding Masked Graph Modeling for Graph Autoencoders has been accepted by KDD 2023!

• [IJCAI 2023] April 20, 2023: our collaboration with Ant Group on Semi-Supervised Anomaly Detection has been accepted by IJCAI 2023!

• [AAAI 2023] November 19, 2022: our work on the use of spiking neural networks to scale up dynamic graph representation learning has been accepted by AAAI 2023 Oral!

• [TKDE 2022] November 4, 2022: our work 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 will give a tutorial about Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection with our collaborators.

• [IJCAI 2022] April 21, 2022: our work 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. 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]

2. 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]

3. 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]

4. 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]

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

6. 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]

7. 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]

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

2. 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]

3. GUARD: Graph Universal Adversarial Defense
   Jintang Li, Jie Liao, Ruofan Wu, Liang Chen*, Jiawang Dan, Changhua Meng, Zibin Zheng, Weiqiang Wang.
   arXiv, 2022.
   [pdf] [code]

4. 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]

5. 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] [github]

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

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

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

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

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

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

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

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

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

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

1. National Scholarship: 2022 in Sun Yat-sen University

Professional services

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

Useful Links

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