通过动态图变压器捕捉结构与时间耦合，提升动态图异常边检测能力。
Structural-Temporal Coupling Anomaly Detection with Dynamic Graph Transformer
- 从结构与时间双维度融合特征，构建异常感知的图演化模式。
- 引入二维位置编码的动态图变压器，同时捕捉判别与上下文一致性信号。
- 在六个数据集上优于现有模型，适合社交、金融等实时异常检测场景。
在演化三元组数据（如社交网络、交易管理、流行病学）中，检测动态图中的异常边是一项重要任务。现有方法多独立处理结构与时间特征，忽略二者深层交互，导致表征能力不足。本文提出一种基于动态图变压器的结构-时间耦合异常检测架构，通过两个整合层级引入结构与时间特征，生成异常感知的图演化模式；并设计带二维位置编码的动态图变压器，有效捕获判别性与上下文一致性信号。在六个数据集上的大量实验表明，该方法显著优于当前最先进模型。案例研究进一步验证了其在真实任务中的有效性。
原文摘要 · Abstract (English)
Detecting anomalous edges in dynamic graphs is an important task in many applications over evolving triple-based data, such as social networks, transaction management, and epidemiology. A major challenge with this task is the absence of structural-temporal coupling information, which decreases the ability of the representation to distinguish anomalies from normal instances. Existing methods focus on handling independent structural and temporal features with embedding models, which ignore the deep interaction between these two types of information. In this paper, we propose a structural-temporal coupling anomaly detection architecture with a dynamic graph transformer model. Specifically, we introduce structural and temporal features from two integration levels to provide anomaly-aware graph evolutionary patterns. Then, a dynamic graph transformer enhanced by two-dimensional positional encoding is implemented to capture both discrimination and contextual consistency signals. Extensive experiments on six datasets demonstrate that our method outperforms current state-of-the-art models. Finally, a case study illustrates the strength of our method when applied to a real-world task.
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