Preprint

• CAP: Co-Adversarial Perturbation on Weights and Features for Improving Generalization of Graph Neural Networks
  Haotian Xue, Kaixiong Zhou, Tianlong Chen, Kai Guo, Xia Hu, Yi Chang, Xin Wang
  

Conference/Journal Papers (* indicates equal contributions)

• Investigating Out-of-Distribution Generalization of GNNs: An Architecture Perspective
  Kai Guo, Hongzhi Wen, Wei Jin, Yaming Guo, Jiliang Tang, Yi Chang.
  KDD, 2024.
  
• Breaking the Curse of Dimensional Collapse in Graph Contrastive Learning: A Whitening Perspective
  Yang Tao, Kai Guo, Yizhen Zheng, Shirui Pan, Xiaofeng Cao, Yi Chang
  Information Sciences, 2023.
  
• Taming Over-Smoothing Representation on Heterophilic Graphs
  Kai Guo, Xiaofeng Cao, Zhining Liu, Yi Chang.
  Information Sciences, 2023.
  
• 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.