Learning Feature Encoder with Synthetic Anomalies for Weakly Supervised Graph Anomaly Detection

ArXi:2605.11749v1 Announce Type: new Weakly supervised graph anomaly detection aims to unveil unusual graph instances, e.g., nodes, whose behaviors significantly differ from normal ones, given only a limited number of annotated anomalies and abundant unlabeled samples. A major challenge is to learn a meaningful latent feature representation that reduces intra-class variance among normal data while remaining highly sensitive to anomalies.