SCN_GNN: A GNN-based fraud detection algorithm combining strong node and graph topology information

Abstract

Graph neural networks (GNNs) have exhibited remarkable success in fraud detection. However, detecting fraud in datasets with scattered graphic densities and multiple relations remains challenging. This is a prevalent issue in fraud detection as fraudsters often employ diverse relationship types to camouflage their activities. Moreover, the constrained interconnectivity between nodes contributes to a scarcity of informative data, thereby intensifying the influence of raw features and further compounding the difficulties in fraud detection processes. To address these challenges, we present SCN_GNN (Strongly Connected Nodes-Graph Neural Network), a novel algorithm for fraud detection, that proposes two node sampling strategies based on the fusion of strong node information and graph topology information. Among them, the structured similarity-aware module (SSAM) performs up-sampling to add useful nodes to the sparse graph, while the strong node module (SNM) performs down-sampling based on strong node information and original features. Furthermore, we also reconfigure the RSRL (Recursive and Scalable Reinforcement Learning framework) module to improve fraud detection performance by increasing inter-class distances and decreasing intra-class distances, resulting in a refined decision boundary for optimized algorithmic efficacy. We use three metrics (AUC, recall, and G_Mean) to evaluate the performance of SCN_GNN. The experimental results compared with the state-of-the-art models on two real-world datasets demonstrate the superiority of the proposed SCN_GNN.