Preprint

• Learning on Graphs with Large Language Models (LLMs): A Deep Dive into Model Robustness [pdf]
  Kai Guo*, Zewen Liu*, Zhikai Chen, Hongzhi Wen, Wei Jin, Jiliang Tang, Yi Chang

Conference/Journal Papers (* indicates equal contributions)

See full list at Google Scholar.

• Empowering GraphRAG with Knowledge Filtering and Integration
  Kai Guo, Harry Shomer, Shenglai Zeng, Haoyu Han, Yu Wang, Jiliang Tang.
  EMNLP, 2025
  
• Towards Context-Robust LLMs: A Gated Representation Fine-tuning Approach
  Shenglai Zeng, Pengfei He, Kai Guo^†, Tianqi Zheng, Hanqing Lu, Yue Xing, Hui Liu.
  ACL, 2025 (Corresponding Author)
  
• Investigating Out-of-Distribution Generalization of GNNs: An Architecture Perspective
  Kai Guo, Hongzhi Wen, Wei Jin, Yaming Guo, Jiliang Tang, Yi Chang.
  KDD, 2024.
  
• Out-of-Distribution Generalization of Federated Learning via Implicit Invariant Relationships
  Yaming Guo*, Kai Guo*, Xiaofeng Cao, Tieru Wu, Yi Chang.
  ICML, 2023
  
• Orthogonal graph neural networks
  Kai Guo, Kaixiong Zhou, Xia Hu, Yu Li, Yi Chang, Xin Wang.
  AAAI, 2022 (Oral)