I'm currently a CS PhD student from Michigan State University. My research interest lies in graph machine learning (past & current & future), symmetry-inspired machine learning (current & future), and probabilistic machine learning (future). A summarization of my past works:

Synergizing large language models and graph machine learning

(: Personally, I don't like these lines of work much, but they earn me lots of citations

• 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
• 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)
• 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
• 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
• A new paper on LLM & graph & database (still in the anonymous period)

Graph Foundation Model

• 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); * means equal contribution
• 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)
• 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

Foundational theory of graph machine learning

• 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)
• Neural Scaling Laws on Graphs Jingzhe Liu, Haitao Mao, Zhikai Chen, Tong Zhao, Neil Shah, Jiliang Tang 2024

Application of graph machine learning

• Leveraging Diversity-Aware Context Attention Networks for Fake News Detection on Social Platforms Zhikai Chen, Peng Wu, Li Pan; IJCNN 2022 Code
• 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