SDGAD combines residual event representations, a two-hypersphere restriction loss, and a normalizing-flow boundary to detect dynamic-graph anomalies with little or no supervision.
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Learning Discriminative and Generalizable Anomaly Detector for Dynamic Graph with Limited Supervision
SDGAD combines residual event representations, a two-hypersphere restriction loss, and a normalizing-flow boundary to detect dynamic-graph anomalies with little or no supervision.