BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs. - Kay Liu; Yingtong Dou; Yue Zhao 016; Xueying Ding; Xiyang Hu; Ruitong Zhang; Kaize Ding; Canyu Chen; Hao Peng 001; Kai Shu; Lichao Sun 001; Jundong Li; et al. (NeurIPS, 2022)

PyGOD: A Python Library for Graph Outlier Detection. - Kay Liu; Yingtong Dou; Yue Zhao 016; Xueying Ding; Xiyang Hu; Ruitong Zhang; Kaize Ding; Canyu Chen; Hao Peng 001; Kai Shu; George H. Chen; Zhihao Jia; et al. (CoRR, 2022)

Benchmarking Node Outlier Detection on Graphs. - Kay Liu; Yingtong Dou; Yue Zhao 016; Xueying Ding; Xiyang Hu; Ruitong Zhang; Kaize Ding; Canyu Chen; Hao Peng 001; Kai Shu; Lichao Sun 001; Jundong Li; et al. (CoRR, 2022)

Equal Opportunity of Coverage in Fair Regression. - Fangxin Wang 003; Lu Cheng 001; Ruocheng Guo; Kay Liu; Philip S. Yu (NeurIPS, 2023)

Data Augmentation for Supervised Graph Outlier Detection with Latent Diffusion Models. - Kay Liu; Hengrui Zhang; Ziqing Hu; Fangxin Wang 003; Philip S. Yu (CoRR, 2023)

Data Augmentation for Supervised Graph Outlier Detection via Latent Diffusion Models. - Kay Liu; Hengrui Zhang; Ziqing Hu; Fangxin Wang 003; Philip S. Yu (LoG, 2024)

Multitask Active Learning for Graph Anomaly Detection. - Wenjing Chang; Kay Liu; Kaize Ding; Philip S. Yu; Jianjun Yu (CoRR, 2024)

Confidence-aware Fine-tuning of Sequential Recommendation Systems via Conformal Prediction. - Chen Wang 018; Fangxin Wang 003; Ruocheng Guo; Yueqing Liang; Kay Liu; Philip S. Yu (CoRR, 2024)

Overcoming Pitfalls in Graph Contrastive Learning Evaluation: Toward Comprehensive Benchmarks. - Qian Ma 012; Hongliang Chi; Hengrui Zhang; Kay Liu; Zhiwei Zhang; Lu Cheng 001; Suhang Wang; Philip S. Yu; Yao Ma 001 (CoRR, 2024)

FedGraph: A Research Library and Benchmark for Federated Graph Learning. - Yuhang Yao; Yuan Li 032; Xinyi Fan; Junhao Li; Kay Liu; Weizhao Jin; Srivatsan Ravi; Philip S. Yu; Carlee Joe-Wong (CoRR, 2024)

LEGO-Learn: Label-Efficient Graph Open-Set Learning. - Haoyan Xu; Kay Liu; Zhengtao Yao; Philip S. Yu; Kaize Ding; Yue Zhao 016 (CoRR, 2024)

PyGOD: A Python Library for Graph Outlier Detection. - Kay Liu; Yingtong Dou; Xueying Ding; Xiyang Hu; Ruitong Zhang; Hao Peng 001; Lichao Sun 001; Philip S. Yu (J. Mach. Learn. Res., 2024)

CoSENT: Consistent Sentence Embedding via Similarity Ranking. - Xiang Huang; Hao Peng 001; Dongcheng Zou; Zhiwei Liu 001; Jianxin Li 002; Kay Liu; Jia Wu 001; Jianlin Su; Philip S. Yu (IEEE ACM Trans. Audio Speech Lang. Process., 2024)

Uncertainty in Graph Neural Networks: A Survey. - Fangxin Wang 003; Yuqing Liu 003; Kay Liu; Yibo Wang 001; Sourav Medya; Philip S. Yu (Trans. Mach. Learn. Res., 2024)

TestNUC: Enhancing Test-Time Computing Approaches and Scaling through Neighboring Unlabeled Data Consistency. - Henry Peng Zou; Zhengyao Gu; Yue Zhou; Yankai Chen 001; Weizhi Zhang 001; Liancheng Fang; Yibo Wang 001; Yangning Li; Kay Liu; Philip S. Yu (ACL (1), 2025)

BANGS: Game-theoretic Node Selection for Graph Self-Training. - Fangxin Wang 003; Kay Liu; Sourav Medya; Philip S. Yu (ICLR, 2025)

TGTOD: A Global Temporal Graph Transformer for Outlier Detection at Scale. - Kay Liu; Jiahao Ding; MohamadAli Torkamani; Philip S. Yu (PAKDD (2), 2025)

Enhancing Fairness in Unsupervised Graph Anomaly Detection through Disentanglement. - Wenjing Chang; Kay Liu; Philip S. Yu; Jianjun Yu (Trans. Mach. Learn. Res., 2025)

LEGO-Learn: Label-Efficient Graph Open-Set Learning. - Haoyan Xu; Kay Liu; Zhengtao Yao; Philip S. Yu; Mengyuan Li; Kaize Ding; Yue Zhao 016 (Trans. Mach. Learn. Res., 2025)

TestNUC: Enhancing Test-Time Computing Approaches through Neighboring Unlabeled Data Consistency. - Henry Peng Zou; Zhengyao Gu; Yue Zhou; Yankai Chen 001; Weizhi Zhang 001; Liancheng Fang; Yibo Wang 001; Yangning Li; Kay Liu; Philip S. Yu (CoRR, 2025)