A multi-task scheme with synthetic anomalies from graph perturbations and two-phase training learns robust features for weakly supervised graph anomaly detection, outperforming competitors on public datasets.
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Learning Feature Encoder with Synthetic Anomalies for Weakly Supervised Graph Anomaly Detection
A multi-task scheme with synthetic anomalies from graph perturbations and two-phase training learns robust features for weakly supervised graph anomaly detection, outperforming competitors on public datasets.