Kay Liu

According to our database, Kay Liu authored at least 23 papers between 2022 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

DIG to Heal: Scaling General-purpose Agent Collaboration via Explainable Dynamic Decision Paths.

Hanqing Yang, Hyungwoo Lee, Yuhang Yao, Zhiwei Liu, Kay Liu, Jingdi Chen, Carlee Joe-Wong

CoRR, March, 2026

Community-aware multi-granularity contrastive learning for graph anomaly detection.

Panpan Meng, Chenxu Wang, Shihao Wang, Kay Liu, Yankai Chen, Zhanggong Wang, Philip S. Yu

Expert Syst. Appl., 2026

2025

Overcoming Pitfalls in Graph Contrastive Learning Evaluation: Toward Comprehensive Benchmarks.

Qian Ma, Hongliang Chi, Hengrui Zhang, Kay Liu, Zhiwei Zhang, Lu Cheng, Suhang Wang, Philip S. Yu, Yao Ma

SIGKDD Explor., December, 2025

TAGFN: A Text-Attributed Graph Dataset for Fake News Detection in the Age of LLMs.

Kay Liu, Yuwei Han, Haoyan Xu, Henry Peng Zou, Yue Zhao, Philip S. Yu

CoRR, November, 2025

TestNUC: Enhancing Test-Time Computing Approaches through Neighboring Unlabeled Data Consistency.

Henry Peng Zou, Zhengyao Gu, Yue Zhou, Yankai Chen, Weizhi Zhang, Liancheng Fang, Yibo Wang, Yangning Li, Kay Liu, Philip S. Yu

CoRR, February, 2025

LEGO-Learn: Label-Efficient Graph Open-Set Learning.

Haoyan Xu, Kay Liu, Zhengtao Yao, Philip S. Yu, Mengyuan Li, Kaize Ding, Yue Zhao

Trans. Mach. Learn. Res., 2025

Enhancing Fairness in Unsupervised Graph Anomaly Detection through Disentanglement.

Wenjing Chang, Kay Liu, Philip S. Yu, Jianjun Yu

Trans. Mach. Learn. Res., 2025

TGTOD: A Global Temporal Graph Transformer for Outlier Detection at Scale.

Kay Liu, Jiahao Ding, MohamadAli Torkamani, Philip S. Yu

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2025

BANGS: Game-theoretic Node Selection for Graph Self-Training.

Fangxin Wang, Kay Liu, Sourav Medya, Philip S. Yu

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

TestNUC: Enhancing Test-Time Computing Approaches and Scaling through Neighboring Unlabeled Data Consistency.

Henry Peng Zou, Zhengyao Gu, Yue Zhou, Yankai Chen, Weizhi Zhang, Liancheng Fang, Yibo Wang, Yangning Li, Kay Liu, Philip S. Yu

Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2025

2024

Uncertainty in Graph Neural Networks: A Survey.

Fangxin Wang, Yuqing Liu, Kay Liu, Yibo Wang, Sourav Medya, Philip S. Yu

Trans. Mach. Learn. Res., 2024

CoSENT: Consistent Sentence Embedding via Similarity Ranking.

Xiang Huang, Hao Peng, Dongcheng Zou, Zhiwei Liu, Jianxin Li, Kay Liu, Jia Wu, Jianlin Su, Philip S. Yu

IEEE ACM Trans. Audio Speech Lang. Process., 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

J. Mach. Learn. Res., 2024

LEGO-Learn: Label-Efficient Graph Open-Set Learning.

Haoyan Xu, Kay Liu, Zhengtao Yao, Philip S. Yu, Kaize Ding, Yue Zhao

CoRR, 2024

FedGraph: A Research Library and Benchmark for Federated Graph Learning.

Yuhang Yao, Yuan Li, Xinyi Fan, Junhao Li, Kay Liu, Weizhao Jin, Srivatsan Ravi, Philip S. Yu, Carlee Joe-Wong

CoRR, 2024

Confidence-aware Fine-tuning of Sequential Recommendation Systems via Conformal Prediction.

Chen Wang, Fangxin Wang, Ruocheng Guo, Yueqing Liang, Kay Liu, Philip S. Yu

CoRR, 2024

Multitask Active Learning for Graph Anomaly Detection.

Wenjing Chang, Kay Liu, Kaize Ding, Philip S. Yu, Jianjun Yu

CoRR, 2024

Data Augmentation for Supervised Graph Outlier Detection via Latent Diffusion Models.

Kay Liu, Hengrui Zhang, Ziqing Hu, Fangxin Wang, Philip S. Yu

Proceedings of the Learning on Graphs Conference, 26-29 November 2024, Virtual., 2024

2023

Data Augmentation for Supervised Graph Outlier Detection with Latent Diffusion Models.

Kay Liu, Hengrui Zhang, Ziqing Hu, Fangxin Wang, Philip S. Yu

CoRR, 2023

Equal Opportunity of Coverage in Fair Regression.

Fangxin Wang, Lu Cheng, Ruocheng Guo, Kay Liu, Philip S. Yu

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Benchmarking Node Outlier Detection on 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

CoRR, 2022

PyGOD: A Python Library for Graph Outlier Detection.

Kay Liu, Yingtong Dou, Yue Zhao, Xueying Ding, Xiyang Hu, Ruitong Zhang, Kaize Ding, Canyu Chen, Hao Peng, Kai Shu, George H. Chen, Zhihao Jia, Philip S. Yu

CoRR, 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

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022