解决动态图中正常行为变化导致的误判问题
Robust Anomaly Detection Under Normality Distribution Shift in Dynamic Graphs
- 通过估计分布统计量并做白化变换，对齐不同时期的正常边嵌入
- 在四个数据集上优于9个基线方法，显著提升异常检测准确率
- 适合处理正常模式随时间演变的动态图场景
动态图中的异常检测在社交网络、电商和网络安全等领域具有广泛应用。现有方法通常假设正常模式保持稳定，但现实中正常行为会随时间演变，这种现象称为正常分布偏移（NDS）。忽略NDS会导致模型将正常变化误判为异常，降低检测性能。为此，我们提出WhENDS，一种新的无监督异常检测方法，通过估计分布统计量并应用白化变换，实现不同时期正常边嵌入的对齐。在四个常用动态图数据集上的大量实验表明，WhENDS持续优于九个强基线方法，达到当前最优效果，凸显了应对NDS在动态图异常检测中的重要性。
原文摘要 · Abstract (English)
Anomaly detection in dynamic graphs is a critical task with broad real-world applications, including social networks, e-commerce, and cybersecurity. Most existing methods assume that normal patterns remain stable over time; however, this assumption often fails in practice due to the phenomenon we refer to as normality distribution shift (NDS), where normal behaviors evolve over time. Ignoring NDS can lead models to misclassify shifted normal instances as anomalies, degrading detection performance. To tackle this issue, we propose WhENDS, a novel unsupervised anomaly detection method that aligns normal edge embeddings across time by estimating distributional statistics and applying whitening transformations. Extensive experiments on four widely-used dynamic graph datasets show that WhENDS consistently outperforms nine strong baselines, achieving state-of-the-art results and underscoring the importance of addressing NDS in dynamic graph anomaly detection.
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