Fraud Detection Through Large-Scale Graph Clustering with Heterogeneous Link Transformation

• URL: http://arxiv.org/abs/2512.19061v1
• Date: Mon, 22 Dec 2025 05:59:13 GMT
• Title: Fraud Detection Through Large-Scale Graph Clustering with Heterogeneous Link Transformation
• Authors: Chi Liu,
• Abstract summary: Collaborative fraud, where multiple accounts coordinate to exploit online payment systems, poses significant challenges.<n>Traditional detection methods that rely solely on high-confidence identity links suffer from limited coverage.<n>We propose a novel graph-based fraud detection framework that addresses the challenge of large-scale heterogeneous graph clustering.
• Score: 3.4057438602175742
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Collaborative fraud, where multiple fraudulent accounts coordinate to exploit online payment systems, poses significant challenges due to the formation of complex network structures. Traditional detection methods that rely solely on high-confidence identity links suffer from limited coverage, while approaches using all available linkages often result in fragmented graphs with reduced clustering effectiveness. In this paper, we propose a novel graph-based fraud detection framework that addresses the challenge of large-scale heterogeneous graph clustering through a principled link transformation approach. Our method distinguishes between \emph{hard links} (high-confidence identity relationships such as phone numbers, credit cards, and national IDs) and \emph{soft links} (behavioral associations including device fingerprints, cookies, and IP addresses). We introduce a graph transformation technique that first identifies connected components via hard links, merges them into super-nodes, and then reconstructs a weighted soft-link graph amenable to efficient embedding and clustering. The transformed graph is processed using LINE (Large-scale Information Network Embedding) for representation learning, followed by HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise) for density-based cluster discovery. Experiments on a real-world payment platform dataset demonstrate that our approach achieves significant graph size reduction (from 25 million to 7.7 million nodes), doubles the detection coverage compared to hard-link-only baselines, and maintains high precision across identified fraud clusters. Our framework provides a scalable and practical solution for industrial-scale fraud detection systems.

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