A fraud detection method based on enhanced graph contrastive learning.
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| Title: | A fraud detection method based on enhanced graph contrastive learning. |
|---|---|
| Authors: | GAO, Yihui1, LI, Yuanqing1, ZHANG, Sanfeng1,2 sfzhang@seu.edu.cn, YANG, Wang1,2 |
| Source: | Computer Engineering & Science / Jisuanji Gongcheng yu Kexue. May2026, Vol. 48 Issue 5, p844-853. 10p. |
| Subjects: | Graph neural networks, Data augmentation, Commercial crimes, Graph algorithms, Heterogeneity |
| 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 dynamically 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. [ABSTRACT FROM AUTHOR] |
| Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 194237697 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A fraud detection method based on enhanced graph contrastive learning. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22GAO%2C+Yihui%22">GAO, Yihui</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22LI%2C+Yuanqing%22">LI, Yuanqing</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22ZHANG%2C+Sanfeng%22">ZHANG, Sanfeng</searchLink><relatesTo>1,2</relatesTo><i> sfzhang@seu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22YANG%2C+Wang%22">YANG, Wang</searchLink><relatesTo>1,2</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Computer+Engineering+%26+Science+%2F+Jisuanji+Gongcheng+yu+Kexue%22">Computer Engineering & Science / Jisuanji Gongcheng yu Kexue</searchLink>. May2026, Vol. 48 Issue 5, p844-853. 10p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Graph+neural+networks%22">Graph neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Data+augmentation%22">Data augmentation</searchLink><br /><searchLink fieldCode="DE" term="%22Commercial+crimes%22">Commercial crimes</searchLink><br /><searchLink fieldCode="DE" term="%22Graph+algorithms%22">Graph algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Heterogeneity%22">Heterogeneity</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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 dynamically 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. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3969/j.issn.1007-130X.2026.05.008 Languages: – Code: chi Text: Chinese PhysicalDescription: Pagination: PageCount: 10 StartPage: 844 Subjects: – SubjectFull: Graph neural networks Type: general – SubjectFull: Data augmentation Type: general – SubjectFull: Commercial crimes Type: general – SubjectFull: Graph algorithms Type: general – SubjectFull: Heterogeneity Type: general Titles: – TitleFull: A fraud detection method based on enhanced graph contrastive learning. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: GAO, Yihui – PersonEntity: Name: NameFull: LI, Yuanqing – PersonEntity: Name: NameFull: ZHANG, Sanfeng – PersonEntity: Name: NameFull: YANG, Wang IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 1007130X Numbering: – Type: volume Value: 48 – Type: issue Value: 5 Titles: – TitleFull: Computer Engineering & Science / Jisuanji Gongcheng yu Kexue Type: main |
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