Yazheng Zhao — accepted AI-conference papers

2026

Graph Anomaly Detection via Feature Selection with Local Topological Residuals

Yazheng Zhao, Nannan Wu, Haoran Yin, Yiming Zhao

IJCAI 2026

Graph anomaly detection (GAD) aims to identify nodes that exhibit significant deviations from expected structural or attribute patterns, and has garnered increasing attention in recent years. Recent approaches for GAD have predominantly focused on local inconsistency mining, which refers to the diff

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2025

Federated Graph Anomaly Detection Through Contrastive Learning with Global Negative Pairs

Nannan Wu, Yazheng Zhao, Hongdou Dong, Keao Xi, Wei Yu, Wenjun Wang

AAAI 2025technical

Anomaly detection on attributed graphs has applications in various domains such as finance and email spam detection, thus gaining substantial attention. Distributed scenarios can also involve issues related to anomaly detection in attribute graphs, such as in medical scenarios. However, most of the…

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