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cs.LG2025
DP-DGAD: A Generalist Dynamic Graph Anomaly Detector with Dynamic Prototypes
Jialun Zheng, Jie Liu, Jiannong Cao +4
Dynamic graph anomaly detection (DGAD) is essential for identifying anomalies in evolving graphs across domains such as finance, traffic, and social networks. Recently, generalist…
cs.LG2024★ 2 cited
Addressing Graph Heterogeneity and Heterophily from A Spectral Perspective
Kangkang Lu, Yanhua Yu, Zhiyong Huang +6
Graph neural networks (GNNs) have demonstrated excellent performance in semi-supervised node classification tasks. Despite this, two primary challenges persist: heterogeneity and h…