Hello! I am a third-year Ph.D. student in Computer Science at Michigan State University, co-advised by Dr. Hui Liu and Dr. Jiliang Tang. Before joining MSU, I obtained my M.S. in Software Engineering from Huazhong University of Science and Technology. My research focuses on graph machine learning and large language models, with a particular emphasis on the intersection of the two. I explore ways to bridge these fields through large-scale pretraining, self-supervised learning, and graph serialization, and I apply these methods to tasks such as link prediction, recommender systems, and anomaly detection. I am especially interested in research that delivers real-world impact, including improvements in efficiency and generalization. If you are interested in any of these topics, feel free to reach out as I am always open to collaboration.

📝 Publications

• A Scalable Pretraining Framework for Link Prediction with Efficient Adaptation, Yu Song, Zhigang Hua, Harry Shomer, Yan Xie, Jingzhe Liu, Bo Long, Hui Liu, KDD 2025

• Higher-order Structure Boosts Link Prediction on Temporal Graphs, Jingzhe Liu, Zhigang Hua, Yan Xie, Bingheng Li, Harry Shomer, Yu Song, Kaveh Hassani, Jiliang Tang, Preprint 2025

• Text-space Graph Foundation Models: Comprehensive Benchmarks and Insights, Zhikai Chen, Haitao Mao, Jingzhe Liu, Yu Song, Bingheng Li, Wei Jin, Bahare Fatemi, Anton Tsitsulin, Bryan Perozzi, Hui Liu, Jiliang Tang, NeurIPS 2024

• Do Neural Scaling Laws Exist on Graph Self-Supervised Learning?, Qian Ma, Haitao Mao, Jingzhe Liu, Zhehua Zhang, Chunlin Feng, Yu Song, Yihan Shao, Yao Ma, LoG 2024

• 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

• Graph Machine Learning in the Era of Large Language Models (LLMs), Shijie Wang, Jiani Huang, Zhikai Chen, Yu Song, Wenzhuo Tang, Haitao Mao, Wenqi Fan, Hui Liu, Xiaorui Liu, Dawei Yin, Qing Li, ACM TIST 2024

• Exploring Adapter-based Transfer Learning for Recommender Systems: Empirical Studies and Practical Insights, Junchen Fu, Fajie Yuan, Yu Song, Zheng Yuan, Mingyue Cheng, Shenghui Cheng, Jiaqi Zhang, Jie Wang, Yunzhu Pan, WSDM 2024

• Where to Go Next for Recommender Systems? ID-vs. Modality-based Recommender Models Revisited, Zheng Yuan, Fajie Yuan, Yu Song, Youhua Li, Junchen Fu, Fei Yang, Yunzhu Pan, Yongxin Ni, SIGIR 2023

• Learning on Graphs with Out-of-Distribution Nodes, Yu Song, Donglin Wang, KDD 2022

💻 Internships

• 2024.08 - 2025.08, Meta, Mentor: Zhigang Hua and Yan Xie.

📚 Teaching

• Fall 2025, Teaching Assistant, Introduction to Artificial Intelligence