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

• URL: http://arxiv.org/abs/2605.11749v1
• Date: Tue, 12 May 2026 08:27:06 GMT
• Title: Learning Feature Encoder with Synthetic Anomalies for Weakly Supervised Graph Anomaly Detection
• Abstract summary: We introduce a weakly supervised graph anomaly detection method that leverages a feature learning strategy tailored for graph anomalies.<n>Our approach is built upon a multi-task learning scheme that extracts robust feature representations through synthesized anomalies.<n>Experiments on public datasets demonstrate the superior performance of our method over its competitors.
• Score: 66.0756595768692
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. Although recent works have applied self-supervised feature learning for graph anomaly detection, their strategies are not specifically tailored to its unique requirements, motivating our exploration of a more domain-specific approach. In this paper, we introduce a weakly supervised graph anomaly detection method that leverages a feature learning strategy tailored for graph anomalies. Our approach is built upon a multi-task learning scheme that extracts robust feature representations through synthesized anomalies. We generate synthetic anomalies by perturbing the normal graph in various ways and assign a dedicated detection head to each anomaly type, ensuring that learned features are sensitive to potential deviations from normal patterns. Although synthetic anomalies may not perfectly replicate real-world patterns, they provide valuable auxiliary data for effective feature learnin, much like features learned from ImageNet classification transfer to downstream vision tasks. Additionally, we adopt a two-phase learning strategy: an initial warm-up phase using only synthetic samples, followed by a full-training phase integrating both tasks, to balance the influence of synthetic and real data. Extensive experiments on public datasets demonstrate the superior performance of our method over its competitors. Code is available at https://github.com/yj-zhou/SAWGAD.

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