PAGE: Progressive Anomaly Generation Network for Semi-supervised Graph Anomaly Detection

Semi-supervised graph anomaly detection confronts the fundamental challenge of identifying anomalous nodes that exhibit deviations from normal graph patterns in node attributes or structure connectivity, using a small set of labeled normal nodes. Existing methods fall short in modeling the spectrum of anomaly severity, as they generate anomalies in a single, undifferentiated step. This oversight restricts the detection of complex anomalies. In this paper, we propose a progressive anomaly generation network (PAGE) to overcome this limitation. PAGE enhances complex anomaly detection through progressive attribute-structure perturbation: It first injects attribute noise into selected normal nodes to generate pseudo-anomalous nodes. Then it applies progressive structure perturbation to create hybrid anomalies that simulate the evolution from mild to complex perturbations. Importantly, the proposed anomaly progressive constraint loss enforces that hybrid anomalies exhibit a higher degree of abnormality than primary anomalies, enhancing the model's anomaly quantification capability. PAGE further integrates deviation constraint loss and classification loss to optimize node representations and improve prediction. Extensive experiments on benchmark datasets demonstrate that PAGE significantly outperforms state-of-the-art methods, demonstrating its effectiveness for graph anomaly detection.