AI RESEARCH
DDGAD: Trajectory Dynamics for Diffusion-Based Graph Anomaly Detection
arXiv CS.AI
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ArXi:2605.26446v1 Announce Type: cross Graph anomaly detection (GAD) aims to identify nodes or substructures whose behavior or attributes deviate significantly from the overall pattern in graph-structured data, with critical applications in financial risk control, social network analysis, and cybersecurity. However, existing GCN-based methods suffer from the fundamental problem of contamination propagation, where anomalous nodes pollute the representations of their neighbors through message passing, leading to degraded detection performance.