I'm currently a CS PhD student from Michigan State University. My research interests lie in relational machine learning on structured data and probabilistic generative models.
I'm currently working on building the ecosystem of relational machine learning. Feel free to contact me if you are also interested.
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- 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)
- Neural Scaling Laws on Graphs Jingzhe Liu, Haitao Mao, Zhikai Chen, Tong Zhao, Neil Shah, Jiliang Tang; LOG 2024
- 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
- 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, LOG 2024
- A Pre-training Framework for Relational Data with Information-theoretic Principles Quang Truong, Zhikai Chen, Mingxuan Ju, Tong Zhao, Neil Shah, Jiliang Tang, NeurIPS 2025