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AUC-oriented Graph Neural Network for Fraud Detection

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Qing HeAuthors Info & Claims

WWW '22: Proceedings of the ACM Web Conference 2022

Pages 1311 - 1321

https://doi.org/10.1145/3485447.3512178

Published: 25 April 2022 Publication History

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Abstract

Though Graph Neural Networks (GNNs) have been successful for fraud detection tasks, they suffer from imbalanced labels due to limited fraud compared to the overall userbase. This paper attempts to resolve this label-imbalance problem for GNNs by maximizing the AUC (Area Under ROC Curve) metric since it is unbiased with label distribution. However, maximizing AUC on GNN for fraud detection tasks is intractable due to the potential polluted topological structure caused by intentional noisy edges generated by fraudsters. To alleviate this problem, we propose to decouple the AUC maximization process on GNN into a classifier parameter searching and an edge pruning policy searching, respectively. We propose a model named AO-GNN (Short for AUC-oriented GNN), to achieve AUC maximization on GNN under the aforementioned framework. In the proposed model, an AUC-oriented stochastic gradient is applied for classifier parameter searching, and an AUC-oriented reinforcement learning module supervised by a surrogate reward of AUC is devised for edge pruning policy searching. Experiments on three real-world datasets demonstrate that the proposed AO-GNN patently outperforms state-of-the-art baselines in not only AUC but also other general metrics, e.g. F1-macro, G-means.

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Cited By

Chen CLee CHuang SPeng W(2024)Credit Card Fraud Detection via Intelligent Sampling and Self-supervised LearningACM Transactions on Intelligent Systems and Technology10.1145/364128315:2(1-29)Online publication date: 28-Mar-2024
https://dl.acm.org/doi/10.1145/3641283
Tang HWang CZheng JJiang C(2024)Enabling Graph Neural Networks for Semi-Supervised Risk Prediction in Online Credit Loan ServicesACM Transactions on Intelligent Systems and Technology10.1145/362340115:1(1-24)Online publication date: 16-Jan-2024
https://dl.acm.org/doi/10.1145/3623401
Jiang XQin ZXu JAo XAngélica LLattanzi SMuñoz Medina AAkoglu LGionis AVassilvitskii S(2024)Incomplete Graph Learning via Attribute-Structure Decoupled Variational Auto-EncoderProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635769(304-312)Online publication date: 4-Mar-2024
https://dl.acm.org/doi/10.1145/3616855.3635769
Show More Cited By

Index Terms

AUC-oriented Graph Neural Network for Fraud Detection
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
    2. Machine learning approaches
      1. Neural networks
2. Information systems
  1. Information systems applications
    1. Data mining

Index terms have been assigned to the content through auto-classification.

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Published In

cover image ACM Conferences

WWW '22: Proceedings of the ACM Web Conference 2022

April 2022

3764 pages

ISBN:9781450390965

DOI:10.1145/3485447

Editors:
Frédérique Laforest
INSA Lyon, France
,
Raphaël Troncy
EURECOM, France
,
Elena Simperl
King’s College London, UK
,
Deepak Agarwal
Pinterest, USA
,
Aristides Gionis
KTH Royal Institute of Technology, Sweden
,
Ivan Herman
W3C / retired
,
Lionel Médini
Université Lyon 1, France

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Published: 25 April 2022

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WWW '22: The ACM Web Conference 2022

April 25 - 29, 2022

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Cited By

Chen CLee CHuang SPeng W(2024)Credit Card Fraud Detection via Intelligent Sampling and Self-supervised LearningACM Transactions on Intelligent Systems and Technology10.1145/364128315:2(1-29)Online publication date: 28-Mar-2024
https://dl.acm.org/doi/10.1145/3641283
Tang HWang CZheng JJiang C(2024)Enabling Graph Neural Networks for Semi-Supervised Risk Prediction in Online Credit Loan ServicesACM Transactions on Intelligent Systems and Technology10.1145/362340115:1(1-24)Online publication date: 16-Jan-2024
https://dl.acm.org/doi/10.1145/3623401
Jiang XQin ZXu JAo XAngélica LLattanzi SMuñoz Medina AAkoglu LGionis AVassilvitskii S(2024)Incomplete Graph Learning via Attribute-Structure Decoupled Variational Auto-EncoderProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635769(304-312)Online publication date: 4-Mar-2024
https://dl.acm.org/doi/10.1145/3616855.3635769
Wen JTang XLu J(2024)An imbalanced learning method based on graph tran-smote for fraud detectionScientific Reports10.1038/s41598-024-67550-414:1Online publication date: 17-Jul-2024
https://doi.org/10.1038/s41598-024-67550-4
Zhang CTian HZhang LJiao P(2024)Robust AUC optimization under the supervision of clean dataScientific Reports10.1038/s41598-024-66788-214:1Online publication date: 19-Jul-2024
https://doi.org/10.1038/s41598-024-66788-2
Zhang JLu JTang X(2024)Two-stage GNN-based fraud detection with camouflage identification and enhanced semantics aggregationNeurocomputing10.1016/j.neucom.2023.127108570:COnline publication date: 12-Apr-2024
https://dl.acm.org/doi/10.1016/j.neucom.2023.127108
Chen JChen QJiang FGuo XSha KWang Y(2024)SCN_GNNExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121643237:PCOnline publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.121643
Gong ZWang GSun YLiu QNing YXiong HPeng JElkind E(2023)Beyond homophilyProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/234(2104-2113)Online publication date: 19-Aug-2023
https://dl.acm.org/doi/10.24963/ijcai.2023/234
Gong SZhou JXie CXuan QFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Neighborhood Homophily-based Graph Convolutional NetworkProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615195(3908-3912)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3615195
Chen LWang LXu JChen SWang WZhao WLi QWang LFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Knowledge-inspired Subdomain Adaptation for Cross-Domain Knowledge TransferProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614946(234-244)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614946
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