S. Forouzandeh

Retrieval-Augmented Contrastive Learning for Dynamic Graph Anomaly Detection

DGRA-CL transforms dynamic graphs into temporal sequences, employs time- and context-aware contrastive learning to learn normal node behavior patterns, retrieves similar normal exemplars from a training pool under a strict causality constraint, and fuses them via similarity-weighted aggregation to construct baseline representations.