Grad: Guided Relation Diffusion Generation for Graph Augmentation in Graph Fraud Detection

TL;DR

This paper introduces Grad, a graph augmentation model using relation diffusion and contrastive learning to improve fraud detection accuracy by highlighting subtle differences between fraudulent and benign behaviors.

Contribution

Grad is a novel relation diffusion-based graph augmentation method that enhances fraud detection by generating auxiliary relations and amplifying weak signals.

Findings

Grad outperforms state-of-the-art methods in real-world datasets.

Achieves up to 11.10% increase in AUC and 43.95% in AP.

Effective in scenarios with sophisticated camouflage strategies.

Abstract

Nowadays, Graph Fraud Detection (GFD) in financial scenarios has become an urgent research topic to protect online payment security. However, as organized crime groups are becoming more professional in real-world scenarios, fraudsters are employing more sophisticated camouflage strategies. Specifically, fraudsters disguise themselves by mimicking the behavioral data collected by platforms, ensuring that their key characteristics are consistent with those of benign users to a high degree, which we call Adaptive Camouflage. Consequently, this narrows the differences in behavioral traits between them and benign users within the platform's database, thereby making current GFD models lose efficiency. To address this problem, we propose a relation diffusion-based graph augmentation model Grad. In detail, Grad leverages a supervised graph contrastive learning module to enhance the fraud-benign difference and employs a guided relation diffusion generator to generate auxiliary homophilic relations from scratch. Based on these, weak fraudulent signals would be enhanced during the aggregation process, thus being obvious enough to be captured. Extensive experiments have been conducted on two real-world datasets provided by WeChat Pay, one of the largest online payment platforms with billions of users, and three public datasets. The results show that our proposed model Grad outperforms SOTA methods in both various scenarios, achieving at most 11.10% and 43.95% increases in AUC and AP, respectively. Our code is released at https://github.com/AI4Risk/antifraud and https://github.com/Muyiiiii/WWW25-Grad.