Ziyang Qiu

Unsupervised Anomaly Detection in Dynamic Graphs via Compatibility Modeling and Boundary Learning

Anomaly detection in dynamic graphs is essential for monitoring evolving systems such as transaction networks and online platforms. Yet existing methods remain limited in realistic edge-stream settings: snapshot-based approaches discretize continuous interactions and miss fine-grained temporal signa