HH-GNN: Homogeneity- and Heterogeneity-Aware Graph Neural Network for Fraud Detection with Noisy Labels

Because graph structures can represent rich information by aggregating neighborhood information, graph neural networks GNNs are heavily used in fraud detection tasks. However, a large amount of noise is generated in fraud detection problems that affect the detection effectiveness of the model. On the one hand, the fraudster actively generates noise through two disguises: feature disguise and relationship disguise on the other hand, a part of the noise is also generated in the graph construction due to the fact that the labeling of the adopted data is not guaranteed to be correct as well as the connection between normal nodes and fraudulent nodes unconsciously. In order to solve the above problems, we propose a framework that focuses on both homogeneous and heterogeneous information HH-GNN in the paper. It improves the noise at graph nodes and connections by considering both homogeneous and heterogeneous information in the distance calculation method and the dilated k-NN algorithm to achieve neighbor aggregation. Meanwhile, based on the early learning phenomenon, we introduce ELR regularization to effectively suppress the influence of noisy labels during gradient descent. Our experiments on graph-based fraud detection tasks on four real datasets using multidimensional metrics of AUC value, and F1-macro demonstrate the effectiveness and superiority of the proposed HH-GNN.