Graph anomaly detection using graph data enhancement under stochastic configuration networks

This study aims to improve the performance of graph data anomaly detection. To address the limitations of traditional methods in terms of accuracy and robustness of anomaly detection, this paper proposes a new method based on randomconfiguration networks and graph data enhancement techniques. The method utilizes random configuration networks forfeature learning on graph data, combined with graph data augmentation methods to enhance the characterizationof thedata to improve the performance of anomaly detection. This paper conducted experimental validation on several publiclyavailable datasets, and the results show that our method outperforms traditional methods in both anomaly detectionperformance. The contribution of this research is to propose a novel and effective approach while bringing randomizedconfiguration networks into graph data anomaly detection is vision.