Hello everyone, my name is Zhikai Chen. Currently, I am a third-year Ph.D. student from Michigan State University, supervised by Professor Jiliang Tang. Previously, I received both my bachelor’s and master’s degrees from Shanghai Jiao Tong University.
My research interest generally lies in understanding the mechanism of graph machine learning models through the lens of controlled empirical experiments, geometry principles, and learning theories. Beyond academia, I mainly study quantitative trading for cryptocurrencies, especially arbitrage and MEV.
📝 Publications
📝 Preprints
- Learning on Graphs with Large Language Models (LLMs): A Deep Dive into Model Robustness Kai Guo, Zewen Liu, Zhikai Chen, Hongzhi Wen, Wei Jin, Jiliang Tang, Yi Chang
- Enhancing ID and Text Fusion via Alternative Training in Session-based Recommendation Juanhui Li, Haoyu Han, Zhikai Chen, Harry Shomer, Wei Jin, Amin Javari, Jiliang Tang 2024
📝 Published
- Unveiling Mode Connectivity in Graph Neural Networks Bingheng Li, Zhikai Chen, Haoyu Han, Shenglai Zeng, Jingzhe Liu, Jiliang Tang; KDD 2025
- Graph Machine Learning in the Era of Large Language Models (LLMs) Wenqi Fan, Shijie Wang, Jiani Huang, Zhikai Chen, Yu Song, Wenzhuo Tang, Haitao Mao, Hui Liu, Xiaorui Liu, Dawei Yin, Qing Li 2024; IEEE TKDE
- Improving Causal Reasoning in Large Language Models: A Survey Longxuan Yu, Delin Chen, Siheng Xiong, Qingyang Wu, Dawei Li, Zhikai Chen, Xiaoze Liu, Liangming Pan; NAACL 2025
- AutoG: Towards automatic graph construction from tabular data Zhikai Chen, Han Xie, Jian Zhang, Xiang Song, Jiliang Tang, Huzefa Rangwala, George Karypis; ICLR 2025 (poster) Code
- A Pure Transformer Pretraining Framework on Text-attributed Graphs Yu Song, Haitao Mao, Jiachen Xiao, Jingzhe Liu, Zhikai Chen, Wei Jin, Carl Yang, Jiliang Tang, Hui Liu; LOG 2024 (poster)
- Neural Scaling Laws on Graphs Jingzhe Liu, Haitao Mao, Zhikai Chen, Tong Zhao, Neil Shah, Jiliang Tang; LOG 2024 (poster)
- Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights Zhikai Chen, Haitao Mao, Jingzhe Liu, Yu Song, Bingheng Li, Wei Jin, Bahare Fatemi, Anton Tsitsulin, Bryan Perozzi, Hui Liu, Jiliang Tang Code; NeurIPS 2024 Datasets and Benchmarks Track (poster)
- Graph Foundation Models Haitao Mao*, Zhikai Chen*, Wenzhuo Tang, Jianan Zhao, Yao Ma, Tong Zhao, Neil Shah, Michael Galkin, Jiliang Tang 2024 [Paper lists]; ICML 2024 (Spotlight)
- Label-free Node Classification on Graphs with Large Language Models (LLMS), Zhikai Chen, Haitao Mao, Hongzhi Wen, Haoyu Han, Wei Jin, Haiyang Zhang, Hui Liu, Jiliang Tang Code; ICLR 2024(poster)
- Exploring the Potential of Large Language Models (LLMs) in Learning on Graphs preprint version journal version, Zhikai Chen, Haitao Mao, Hang Li, Wei Jin, Hongzhi Wen, Xiaochi Wei, Shuaiqiang Wang, Dawei Yin, Wenqi Fan, Hui Liu, Jiliang Tang 2023 Code; SIGKDD Explorations and NeurIPS GLFrontiers 2023
- Demystifying Structural Disparity in Graph Neural Networks: Can One Size Fit All? Haitao Mao, Zhikai Chen, Wei Jin, Haoyu Han, Yao Ma, Tong Zhao, Neil Shah, Jiliang Tang; NeurIPS 2023(poster)
- Leveraging Diversity-Aware Context Attention Networks for Fake News Detection on Social Platforms Zhikai Chen, Peng Wu, Li Pan; IJCNN 2022 Code
📝 Patents
- A Fake News Identification System Based on Heterogeneous Graph Contrastive Learning. CN114020928A,2022.
📝 Organizations
- Web Conference Workshop 2024: Graph Foundation Models Haitao Mao, Jianan Zhao, Xiaoxin He, Zhikai Chen, Qian Huang, Zhaocheng Zhu, Jian Tang, Michael Bronstein, Xavier Bresson, Bryan Hooi, Haiyang Zhang, Xianfeng Tang, Chen Luo, Jiliang Tang
🎖 Honors and Awards
- 2021.05 WAIC insurance multi-modal QA. 5th place/600+ teams
📖 Educations
- 2023.01 - Now, Michigan State University, Computer Science, Ph.D.
- 2020.09 - 2023.01, Shanghai Jiao Tong University, Cyberspace Security, Master Degree.
- 2016.09 - 2020.08, Shanghai Jiao Tong University, Joint Institute, ECE, Bachelor Degree.
📖 Talks
- 2024.02 From LLM4Graph to principled graph foundation models Video Slides LOGS Seminar
- 2023.08 Exploring the potential of LLMs Slides Shandong University, BUPT
💻 Internships
- 2024.06 - 2024.09, Amazon AWS, Santa Clara, Applied Scientist Intern, Mentor: James Zhang
- 2023.05 - 2023.09, Baidu, Beijing, China, Mentor: Xiaochi Wei
- 2021.01 - 2021.05, HK Vision, Shanghai, China
💻 Services
Reviewers for TKDE, TKDD, ACL, KDD (ADS track), JMLR
Student organizers of WWW2024 graph foundation model workshop