Online Test-Time Adaptation for Generalizable Dynamic Graph Anomaly Detection

2026-08-20

AI Summary

为了解决动态图异常检测中域特异性模式丢失和目标数据顺序到达的问题，提出OTTA-DGAD方法，通过动态原型提取和在线测试时自适应更新记忆缓冲区来识别异常。

Abstract

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. First, they mainly rely on domain-agnostic patterns and miss domain-specific patterns that keep evolving. Second, they assume access to the full target domain data, whereas in more practical online test-time adaptation settings, target data arrive sequentially in unlabeled chunks. To address these limitations, we formulate online test-time adaptation for generalizable DGAD and propose OTTA-DGAD. OTTA-DGAD first extracts dynamic prototypes, i.e., evolving representations of normal and anomalous patterns, from temporal ego-graphs and stores them in a memory buffer. The buffer selectively retains general patterns shared across the source domains used for pretraining while incorporating new patterns from the target domain. An anomaly scorer then compares incoming edge representations against these prototypes to identify both general and domain-specific anomalies. During adaptation, OTTA-DGAD updates the memory buffer using reliable pseudo-labels identified through confidence-based detection. It further enriches each target chunk with relevant representations retained from previous chunks, compensating for information loss resulting from the sequential arrival of data. Extensive experiments under strict test-then-adapt OTTA settings demonstrate state-of-the-art performance on ten real-world datasets from diverse domains.

Problem

• Dynamic Graph Anomaly Detection
• Online Test-Time Adaptation
• Domain-Specific Patterns

Innovation

• Online Test-Time Adaptation
• Dynamic Graph Anomaly Detection
• Memory Buffer
• Dynamic Prototypes
• Confidence-based Detection