Graph anomaly detection using graph data enhancement under stochastic configuration networks

Qingyou Tan; Guiping Wang

doi:10.1117/12.3040583

10 September 2024 Graph anomaly detection using graph data enhancement under stochastic configuration networks

Qingyou Tan, Guiping Wang

Proceedings Volume 13257, International Conference on Advanced Image Processing Technology (AIPT 2024); 1325708 (2024) https://doi.org/10.1117/12.3040583
Event: International Conference on Advanced Image Processing Technology (AIPT 2024), 2024, Chongqing, China

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Qingyou Tan and Guiping Wang "Graph anomaly detection using graph data enhancement under stochastic configuration networks", Proc. SPIE 13257, International Conference on Advanced Image Processing Technology (AIPT 2024), 1325708 (10 September 2024); https://doi.org/10.1117/12.3040583

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