Graph Evidential Learning for Anomaly Detection

• URL: http://arxiv.org/abs/2506.00594v1
• Date: Sat, 31 May 2025 15:06:42 GMT
• Title: Graph Evidential Learning for Anomaly Detection
• Authors: Chunyu Wei, Wenji Hu, Xingjia Hao, Yunhai Wang, Yueguo Chen, Bing Bai, Fei Wang,
• Abstract summary: We propose Graph Evidential Learning (GEL) to redefine the reconstruction process through evidential learning.<n>GEL quantifies two types of uncertainty: graph uncertainty and reconstruction uncertainty, incorporating them into the anomaly scoring mechanism.<n>GEL achieves state-of-the-art performance while maintaining high robustness against noise and structural perturbations.
• Score: 19.046244103954855
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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