A Dual-Channel Contrastive Learning Framework for Anomaly Detection in Dynamic Graph Structures

Detecting data that deviates from normal behavior in dynamic graphs is an important hotspot. In the real world, anomaly detection is widely applied across various domains. However, existing anomaly detection methods typically assume static input graphs, failing to capture dynamic information in real-world scenarios. In addition, during the evolution of node representations over time, standard updating mechanisms lack the ability to selectively preserve important features. To address these issues, this paper constructs a dynamic graph anomaly detection framework (DyConAD) that enhances model perception of dynamic information through memory enhancement strategies and multi-scale similarity-based anomaly scoring mechanisms. Additionally, we also design a dynamic graph construction method that extracts time-aware shapelet and establishes spatiotemporal relationships between these shapelet. Experimental validation is conducted on four real-world datasets and three public datasets from the UCR Time Series Archive. Results demonstrate that our proposed method outperforms existing approaches across multiple datasets, achieving effective improvements in both detection accuracy and efficiency.