Structural-Temporal Coupling Anomaly Detection with Dynamic Graph Transformer
This work addresses anomaly detection in dynamic graphs for applications like social networks and transaction management, but it appears incremental as it builds on existing transformer-based methods with specific enhancements.
The paper tackled the problem of detecting anomalous edges in dynamic graphs by addressing the lack of structural-temporal coupling information, and the result was that their method outperformed state-of-the-art models on six datasets.
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.