A Robust Graph Fraud Detection Model Based on Adversarial Reweighting

Graph-based fraud detection has gained significant attention due to its effectiveness in risk management. Recent approaches utilizing graph neural networks highlight the benefits of representing nodes through aggregating homogeneous neighbors. However, in real-world situations, fraudsters disguise their activities by forming connections with benign entities, leading to limitations in the homogeneous aggregation method. Because combining representations from neighborhoods of different categories can dilute the effectiveness of the aggregation. On the other hand, some methods neglect quality of labels and introduce uniform weights for all training nodes, producing challenges for model robustness. To tackle these challenges, we propose a robust model for graph-based fraud detection that leverages adversarial reweighting to improve neighborhood aggregation. In graph-based fraud detection, account nodes can be classified into two types: normal and abnormal. Our model enables dual encoders to assimilate information from both normal and abnormal neighbors, respectively, ensuring resilience against different classes of neighbors. For neighbors without labels, we employ pseudolabels to ascertain the weights for aggregated features across different category spaces. This process still introduces information contamination due to the quality of labeled nodes. To mitigate this issue, we introduce perturbation strategies based on edge structures and node features. These strategies dynamically adjust node weights during training based on the accuracy of model predictions after the introduction of perturbation. This adjustment enhances the contribution of reliable nodes in the loss function, ultimately improving the model’s detection capabilities. Our comprehensive evaluation across four public benchmark datasets-Amazon, Yelp, T-Finance, and T-Social-demonstrates that our proposed method outperforms current state-of-the-art techniques.