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

Weakly supervised graph anomaly detection aims to unveil unusual graph instances, e.g., nodes, whose behaviors are significantly different from the normal ones, under the condition that only a limited number of annotated anomalies but abundant unlabeled samples are available. A major challenge for this task 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 methods for graph anomaly detection, their strategies are not specifically tailored to the unique requirements of graph anomaly detection, which motivates our exploration of a more domain-specific feature learning approach. In this paper, we introduce a weakly supervised graph anomaly detection method that leverages a feature learning strategy specifically tailored for graph anomalies. Our approach is built upon a multi-task learning scheme designed to extract robust feature representations, through synthesized anomalies. We generate these synthetic anomalies by perturbing the normal graph in various ways and assign a dedicated detection head to each anomaly type. This design ensures that the 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 learning—much like the way features learned from classifying ImageNet images are used in various downstream computer vision tasks. Additionally, we adopt a two-phase learning strategy to balance the influence of synthetic anomalies and real data. The process begins with an initial warm-up phase using only synthetic samples, followed by a full-training phase that integrates both tasks. Numerous experiments on public datasets demonstrate the superior performance of our proposed strategy, in comparison with those of its competitors. Code is available at.