I am currently a PhD student at Sun Yat-sen University (SYSU), where I am advised by Prof. Liang Chen. I received the master’s degree from Sun Yat-sen University in 2021. My research interests are in the area of Trustworthy Graph Learning. More specifically, I work on the area of graph neural networks including their theory foundations, reliability and applications.
- [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!
- [TKDE 2022] November 4, 2022: our work on robust graph neural networks has been accepted by TKDE!
- 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.
Sun Yat-sen University
PhD 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.
- Research Intern in Ant Group, from February 2022 to June 2022.
PyTorch is all you need!
- [GraphGallery]: A gallery for benchmarking Graph Neural Networks (GNNs).
GreatX is great!
- [GreatX]: A graph reliability toolbox based on PyTorch and PyTorch Geometric.
- [Mooon]: A graph data augmentation library based on PyTorch and PyTorch Geometric.
- [Awesome Graph Adversarial Learning]: A curated collection of adversarial attack and defense on graph data.
- [Awesome Fair Graph Learning]: Paper Lists for Fair Graph Learning (FairGL).
- [Awesome Masked Autoencoders]: A collection of literature after or concurrent with Masked Autoencoder (MAE) (Kaiming He el al.).
Note: * for corresponding author, # for equal contribution.
A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection. (arXiv 2022).
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.
MaskGAE: Masked Graph Modeling Meets Graph Autoencoders. (arXiv 2022).
Jintang Li#, Ruofan Wu#, Wangbin Sun, Liang Chen*, Sheng Tian, Liang Zhu, Changhua Meng, Zibin Zheng, Weiqiang Wang.
Recent Advances in Reliable Deep Graph Learning: Inherent Noise, Distribution Shift, and Adversarial Attack. (arXiv 2022).
Jintang Li#, Bingzhe Wu#*, Chengbin Hou, Guoji Fu, Yatao Bian, Liang Chen, Junzhou Huang.
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
Association for the Advancement of Artificial Intelligence (AAAI 2023 oral, CCF-A)
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, CCF-A).
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, CCF-A).
Graph Enhanced Neural Interaction Model for recommendation.
Liang Chen, Tao Xie, Jintang Li, Zibin Zheng*.
Knowledge-Based Systems (KBS 2022, CCF-C).
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, CCF-A).
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, CCF-A).
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, demo track (ICSE 2021, CCF-A).
Phishing Scams Detection in Ethereum Transaction Network, ACM Transactions on Internet Technology.
Liang Chen, Jiaying Peng, Yang Liu, Jintang Li, Fenfang Xie, Zibin Zheng.
ACM Transactions on Internet Technology (TOIT 2021)
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)
- Ant Group Green Computing Contest [Link], 2nd place.
- CIKM 2022 AnalytiCup Competition: Federated Hetero-Task Learning [Link] [Code], 4th place.
- ICDM 2022 Competition: Risk Commodities Detection on Large-Scale E-Commence Graphs [Link] [Code], 3rd place.
- FinvCup 2022: Fraud User Risk Identification [Link], [Code], 9th place.
- Ant Group ATEC 2021: truthworthy AI [Link], 2rd place.
- Spectra Review Paper Competition 2022 (Spring) winner [Link].
- KDD Cup 2020, Adversarial Attacks and Defense on Academic Graph [Link], 2nd place.
- Spectra Review Paper Competition 2021 [Link], 3rd place winner, Graph Adversarial Learning.
- Ant Group ATEC 2021 online, Track 2: Fraud detection of digital currency transactions [Link], 4th place.
- KDD 2022 tutorial [Link] Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection
- AI TIME IJCAI 2021 [Link]: Understanding Structural Vulnerability in Graph Convolutional Networks
- National Scholarship: 2022 in Sun Yat-sen University