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Online Test-Time Adaptation for Generalizable Dynamic Graph Anomaly Detection

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Generalizable dynamic graph anomaly detection (DGAD) enables pretrained detectors to identify anomalies in unseen target domains without costly retraining. However, existing methods often fail for two reasons.

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arXiv Machine Learning
4d ago

Temporal Memory-Aware Online Test-Time Adaptation on Dynamic Graphs

The paper introduces DGOTTA, a framework for temporal memory‑aware online test‑time adaptation on dynamic graphs. DGOTTA comprises three modules: temporal‑aware augmentation to diversify test graphs, memory‑aware model prediction to mitigate catastrophic forgetting, and consistency‑guided online adaptation to enforce temporal alignment and smoothness. Experiments on three real‑world datasets and four DGNN backbones show that DGOTTA improves generalization under diverse distribution shifts and across multiple model architectures.

By Bo Li, Xin Zheng, Ming Jin, Can Wang, Shirui Pan