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 of context-dependent deviations without requiring attributes.
Jia-Chi Luo, Sha-Meng Wen, Ziyan Qiu et al.
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