Enhancing Graph-based Fraud Detection by Adversarial Confidence Reweighting

Graph-based fraud detection has emerged as a pivotal tool in risk management, leveraging the power of graph neural networks to enhance node representations by aggregating information from neighboring nodes. However, this aggregation process can sometimes introduce noise by incorporating neighbors from different categories, potentially diluting the central node’s representation. To tackle this issue, we introduce an innovative Adversarial Confidence Reweighting (ACR) technique designed to allocate discriminative weights to samples automatically. This approach effectively minimizes the impact of noisy neighbors on the representation of nodes. By introducing controlled adversarial perturbations to the nodes being classified, we can assess the extent of representation dilution. Furthermore, we employ an adaptive node re-weighted learning objective, which dynamically adjusts node weights based on a confidence measure derived from prediction accuracy. Our experimental evaluations across three public datasets demonstrate that our adversarial strategy significantly surpasses the baseline model in terms of detection performance.