CRC-SGAD: Conformal Risk Control for Supervised Graph Anomaly Detection
This addresses reliability challenges in graph anomaly detection for security-sensitive domains, representing an incremental advance with novel method integration.
The paper tackled the problem of unreliable confidence estimation and adversarial vulnerability in supervised graph anomaly detection by proposing CRC-SGAD, a framework that integrates statistical risk control, resulting in statistically significant improvements in false negative and false positive rate control and prediction set size across four datasets and five models.
Graph Anomaly Detection (GAD) is critical in security-sensitive domains, yet faces reliability challenges: miscalibrated confidence estimation (underconfidence in normal nodes, overconfidence in anomalies), adversarial vulnerability of derived confidence score under structural perturbations, and limited efficacy of conventional calibration methods for sparse anomaly patterns. Thus we propose CRC-SGAD, a framework integrating statistical risk control into GAD via two innovations: (1) A Dual-Threshold Conformal Risk Control mechanism that provides theoretically guaranteed bounds for both False Negative Rate (FNR) and False Positive Rate (FPR) through providing prediction sets; (2) A Subgraph-aware Spectral Graph Neural Calibrator (SSGNC) that optimizes node representations through adaptive spectral filtering while reducing the size of prediction sets via hybrid loss optimization. Experiments on four datasets and five GAD models demonstrate statistically significant improvements in FNR and FPR control and prediction set size. CRC-SGAD establishes a paradigm for statistically rigorous anomaly detection in graph-structured security applications.