Retrieval-Augmented Contrastive Learning for Dynamic Graph Anomaly Detection

Detecting anomalous nodes in continuously evolving graphs without labeled supervision requires representations that capture both local temporal context and globally consistent normal behavior—a combination that current methods do not jointly address. Existing dynamic anomaly detectors rely on localized temporal neighborhoods and cannot leverage globally similar normal patterns elsewhere in the graph, while existing retrieval-augmented graph methods either require labels or do not enforce strict temporal causality during retrieval. We propose DGRA-CL (Dynamic Graph Retrieval-Augmented Contrastive Learning), an unsupervised framework that learns discriminative temporal node representations for anomaly detection without labeled data. 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. Anomalies are detected via deviation-based scoring measuring distance from these normal baselines. On four real-world dynamic graphs, DGRA-CL achieves statistically significant AUC gains of 1–2 points over the strongest baselines on three of four benchmarks (UCI Messages, Bitcoin-Alpha, Digg) and competitive performance on Reddit, while operating without anomaly labels and generalizing to unseen nodes.