Anomaly Detection in Dynamic Graphs Via Long Short-Term Temporal Attention Network

Anomaly detection in dynamic graphs has drawn increasing attention in social networks, e-commerce, and cybersecurity etc. Capturing the evolution patterns using temporal features is crucial for dynamic graphs anomaly detection. However, existing approaches do not thoroughly consider the long-term and short-term temporal features, which lead to the loss of information about the evolution patterns of dynamic graphs. In this paper, we propose a end-to-end an anomaly detection algorithm in dynamic graphs via Long Short-term Temporal Attention network (GraphLSTA). GraphLSTA captures the evolution patterns of dynamic graphs by effectively extracting and fusing the long-term and short-term temporal features through a recurrent attention mechanism, improving the detection performance.