Normality Calibration in Semi-supervised Graph Anomaly Detection
Researchers have developed a new framework called GraphNC to improve semi-supervised graph anomaly detection. This method calibrates normality by leveraging both labeled and unlabeled data, using a teacher model to guide the process. GraphNC incorporates anomaly score distribution alignment and perturbation-based normality regularization to enhance the accuracy and separability of anomaly scores and node representations. AI