AINeutralarXiv – CS AI · 15h ago6/10
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DDGAD: Trajectory Dynamics for Diffusion-Based Graph Anomaly Detection
Researchers introduce DDGAD, a diffusion-based framework for detecting anomalous nodes in graph-structured data that addresses a critical limitation in existing GCN methods: contamination propagation. The model uses trajectory dynamics and reliability-aware mechanisms to distinguish normal from anomalous nodes, with applications in financial risk detection and cybersecurity.