Graph anomaly detection framework based on feature enhancement and contrastive learning

Zhenhao Lei; Peng Pu

doi:10.1117/12.3035518

19 July 2024 Graph anomaly detection framework based on feature enhancement and contrastive learning

Zhenhao Lei, Peng Pu

Author Affiliations +

Proceedings Volume 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024); 132132C (2024) https://doi.org/10.1117/12.3035518
Event: International Conference on Image Processing and Artificial Intelligence (ICIPAl2024), 2024, Suzhou, China

Abstract

Graph anomaly detection plays a key role in many real-world scenarios such as social network flooding detection and financial fraud detection. Graph anomaly detection methods based on contrastive self-supervision have been proven to be effective, but current models lack full utilization of features and lack attention to the over-smoothing problem during training. Therefore, this paper proposes a new contrastive self-supervised model for sampled node and subgraph instance pairs, which fully captures the information of nodes and subgraphs, and at the same time reconstructs the information of subgraphs, which effectively solves the problems of insufficient feature capture and over-smoothing of graph neural network modules. Experiments on three public datasets and three benchmark models demonstrate the superiority of our proposed model.

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

Citation Download Citation

Zhenhao Lei and Peng Pu "Graph anomaly detection framework based on feature enhancement and contrastive learning", Proc. SPIE 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024), 132132C (19 July 2024); https://doi.org/10.1117/12.3035518

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