为图异常检测提供可信的置信度控制，保障安全应用中的检测可靠性。
CRC-SGAD: Conformal Risk Control for Supervised Graph Anomaly Detection
- 采用双阈值分位法生成预测集，理论保证误报率和漏报率上限。
- 在四个数据集上，异常检测的漏报率与误报率显著降低。
- 适合对安全性要求高的图数据异常检测场景，如金融风控、网络安全。
图异常检测（GAD）在安全敏感领域至关重要，但面临可靠性挑战：置信度估计不准（正常节点低估，异常节点高估）、置信度分数对结构扰动易受攻击，以及传统校准方法对稀疏异常模式效果有限。为此，我们提出CRC-SGAD框架，通过两项创新将统计风险控制引入GAD：(1) 双阈值分位法的符合性风险控制机制，通过生成预测集理论上保证误报率（FPR）和漏报率（FNR）的上限；(2) 子图感知的谱图神经校准器（SSGNC），通过自适应谱滤波优化节点表示，并通过混合损失函数减小预测集规模。在四个数据集和五种GAD模型上的实验表明，该方法在FNR和FPR控制及预测集大小方面均实现统计显著提升。CRC-SGAD为图结构化安全应用中的统计严谨异常检测建立了新范式。
原文摘要 · Abstract (English)
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.
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