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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
Detecting Anomalies in Dynamic Graphs via Memory enhanced Normality
Jie Liu, Xuequn Shang, Xiaolin Han +2
Anomaly detection in dynamic graphs presents a significant challenge due to the temporal evolution of graph structures and attributes. The conventional approaches that tackle this…