Graph Evidential Learning for Anomaly Detection

TL;DR

This paper introduces Graph Evidential Learning (GEL), a probabilistic framework for graph anomaly detection that models uncertainties to improve robustness and accuracy over traditional reconstruction-error-based methods.

Contribution

GEL redefines graph reconstruction using evidential distributions, effectively quantifying uncertainties and enhancing anomaly detection robustness and performance.

Findings

GEL achieves state-of-the-art anomaly detection results.

GEL is more robust to noise and structural perturbations.

GEL effectively models uncertainty in graph data.

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.