Liu, Kay

8 publications

ICLR 2025 BANGS: Game-Theoretic Node Selection for Graph Self-Training Fangxin Wang, Kay Liu, Sourav Medya, Philip S. Yu

LoG 2025 Data Augmentation for Supervised Graph Outlier Detection via Latent Diffusion Models Kay Liu, Hengrui Zhang, Ziqing Hu, Fangxin Wang, Philip S. Yu

TMLR 2025 Enhancing Fairness in Unsupervised Graph Anomaly Detection Through Disentanglement Wenjing Chang, Kay Liu, Philip S. Yu, Jianjun Yu

TMLR 2025 LEGO-Learn: Label-Efficient Graph Open-Set Learning Haoyan Xu, Kay Liu, Zhengtao Yao, Philip S. Yu, Mengyuan Li, Kaize Ding, Yue Zhao

MLOSS 2024 PyGOD: A Python Library for Graph Outlier Detection Kay Liu, Yingtong Dou, Xueying Ding, Xiyang Hu, Ruitong Zhang, Hao Peng, Lichao Sun, Philip S. Yu

TMLR 2024 Uncertainty in Graph Neural Networks: A Survey Fangxin Wang, Yuqing Liu, Kay Liu, Yibo Wang, Sourav Medya, Philip S. Yu

NeurIPS 2023 Equal Opportunity of Coverage in Fair Regression Fangxin Wang, Lu Cheng, Ruocheng Guo, Kay Liu, Philip S Yu

NeurIPS 2022 BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs Kay Liu, Yingtong Dou, Yue Zhao, Xueying Ding, Xiyang Hu, Ruitong Zhang, Kaize Ding, Canyu Chen, Hao Peng, Kai Shu, Lichao Sun, Jundong Li, George H Chen, Zhihao Jia, Philip S Yu