提出新方法检测伪装异常，能发现被隐藏的低频异常节点。
Modeling Spectral Energy Shifts in Spatio-Temporal Graph Anomaly Detection
- 基于节点级谱能量建模，兼容消息传递机制
- 在多个数据集上显著提升对伪装异常的检测率
- 无需专用时序模块，可高效处理长滑动窗口
图异常检测旨在识别异常节点。现有方法通常通过谱能量分布变化来刻画异常，但忽略了能量变化减小的情况，即表现为‘伪装异常’——看似正常却实际异常。我们发现这类异常在多个数据集中普遍存在，且现有谱方法无法检测。为此，我们提出一种与消息传递完全兼容的节点级谱能量形式化方法，可有效识别伪装异常。在此基础上，构建了能量感知的图学习框架，通过能量驱动的消息传递，在静态与时序图中建模谱移。该统一架构无需引入专门的序列模块，即可在长滑动窗口下实现高效学习。大规模基准测试验证了该方法的有效性与可扩展性。
原文摘要 · Abstract (English)
Graph anomaly detection methods aim to distinguish anomalous nodes. While prior methods characterize anomalies through increased variation in the spectral energy distributions, they overlook those that result in decreased variation, i.e., camouflaged anomalies that appear normal. We show that this type of anomaly persists across multiple datasets and remains undetectable by existing spectral approaches. To address this limitation, we propose a node-level spectral energy formulation that is fully compatible with message passing and enables the detection of camouflaged anomalies. Building on this formulation, we introduce an energy-aware graph learning framework that models spectral shifts through energy-driven message passing in both static and time-series graphs. Besides, our unified architecture extends to temporal settings without introducing specialized sequence modules, enabling efficient learning under long sliding windows. Extensive experiments on large-scale benchmarks demonstrate the effectiveness and scalability of our approach.
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