I am a fourth-year Ph.D. student from the Department of Artificial Intelligence at Jilin University, working with Prof. Yi Chang. I am currently a visiting scholar at Michigan State University (MSU), under supervision of Prof. Jiliang Tang.

My research interests lie in the broad area of data mining and graph neural networks, particularly in graph analysis and out-of-distribution generalization in graphs.

Email: guokai20 at mails.jlu.edu.cn / guokai1 at msu.edu

News

May. 2024: One paper accepted by KDD, 2024.

Dec. 2023: One paper accepted by Information Sciences, 2023.

Aug. 2023: One paper accepted by Information Sciences, 2023.

Jun. 2023: Begin my visiting at MSU！

Apr. 2023: One paper accepted by ICML, 2023.

Jun. 2022: One paper accepted by AAAI, 2022 (Oral 4.5%).

Selected publication

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.

Service

PC members, and reviewers for multiple journals and international conferences in machine learning and data science such as TKDD, TKDE, KDD and ICLR.