Qi Huang

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

BAD is proposed, an unsupervised framework for anomaly detection in continuous-time dynamic graphs that represents nodes with learnable identity embeddings and performs pairwise compatibility modeling via cross-attention between each destination node and the source’s recent neighbors, enabling direct characterization o...