A fraud detection method based on enhanced graph contrastive learning

GAO Yihui, LI Yuanqing, ZHANG Sanfeng, YANG Wang

doi:10.3969/j.issn.1007-130X.2026.05.008

Computer Engineering & Science >

2026 , Vol. 48 >Issue 5: 844 - 853

DOI: https://doi.org/10.3969/j.issn.1007-130X.2026.05.008

Computer Network and Znformation Security

A fraud detection method based on enhanced graph contrastive learning

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(1.School of Cyber Science and Engineering,Southeast University,Nanjing 211189;
2.Key Laboratory of Computer Network and Information Integration (Southeast University),
Ministry of Education,Nanjing 211189,China)

Received date: 2024-09-20

Revised date: 2025-01-04

Online published: 2026-05-21

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Abstract

Graph contrastive learning, as an effective pre-training strategy, can address the issue of scarce high-quality labeled data in graph-based fraud detection methods. However, current approaches in this category face challenges where malicious behavioral features are either weakened within the aggregation mechanism of graph neural networks or compromised during the data augmentation process. To tackle this, this paper proposes an optimized graph contrastive learning method that integrates graph reconstruction and dynamic data augmentation techniques, aiming to enhance the effectiveness of fraud detection. This method reduces conflicts arising from neighbor feature aggregation by adjusting edge weights in the graph, thereby improving detection accuracy. Simultaneously, it dynam- ically adjusts the data augmentation process using label invariance and distribution diversity metrics to ensure that the augmented data retains critical fraud features while possessing necessary diversity. Experimental results on multiple graph fraud detection datasets demonstrate the effectiveness of this method, with detection performance improvements ranging from 2% to 5% compared to state-of-the-art methods.

Key words： fraud detection; graph contrastive learning; graph data augmentation; graph reconstruction

Cite this article

GAO Yihui, LI Yuanqing, ZHANG Sanfeng, YANG Wang . A fraud detection method based on enhanced graph contrastive learning[J]. Computer Engineering & Science, 2026 , 48(5) : 844 -853 . DOI: 10.3969/j.issn.1007-130X.2026.05.008

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