用证据学习提升图异常检测的鲁棒性，解决无标签数据难题。
Graph Evidential Learning for Anomaly Detection
- 引入证据分布建模节点特征与图结构，量化两类不确定性。
- 在多个数据集上超越现有方法，噪声下仍保持高精度。
- 适合处理无标注图数据的异常检测任务。
图异常检测因缺乏可靠的异常标注数据集而面临挑战，推动了无监督方法的发展。图自编码器（GAE）通过重构图结构和节点特征，并利用重构误差生成异常分数，成为主流方法。然而，仅依赖重构误差会增加对噪声和过拟合的敏感性。为此，我们提出图证据学习（GEL），一种概率框架，通过证据学习重新定义重构过程。通过使用证据分布建模节点特征和图拓扑，GEL量化了图不确定性和重构不确定性，并将其融入异常评分机制。大量实验表明，GEL在保持对噪声和结构扰动高鲁棒性的前提下，实现了最先进的性能。
原文摘要 · Abstract (English)
Graph anomaly detection faces significant challenges due to the scarcity of reliable anomaly-labeled datasets, driving the development of unsupervised methods. Graph autoencoders (GAEs) have emerged as a dominant approach by reconstructing graph structures and node features while deriving anomaly scores from reconstruction errors. However, relying solely on reconstruction error for anomaly detection has limitations, as it increases the sensitivity to noise and overfitting. To address these issues, we propose Graph Evidential Learning (GEL), a probabilistic framework that redefines the reconstruction process through evidential learning. By modeling node features and graph topology using evidential distributions, GEL quantifies two types of uncertainty: graph uncertainty and reconstruction uncertainty, incorporating them into the anomaly scoring mechanism. Extensive experiments demonstrate that GEL achieves state-of-the-art performance while maintaining high robustness against noise and structural perturbations.
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