A GNN-based Fraud Detector with Dual Resistance to Graph Disassortativity and Imbalance

With the prosperity of Internet services, various fraudulent activities have emerged, and some graph neural network-based methods have been proposed for fraud detection. These methods have achieved significant performances based on the assortativity assumption where the connected nodes tend to have the same labels. However, the fraud graphs are not always assortative but more likely disassortative as fraudsters usually disguise themselves by deliberately making extensive connections to benign users. Furthermore, some studies have noticed node imbalance issues as the fraudsters are far fewer than normal users. But for another one, the edge imbalance issue is unexplored, and they also failed to make full use of these rare but valuable edges. Therefore, we propose an imbalanced disassortative graph learning framework for fraud detection. Specifically, a learnable dual-channel graph convolution filter is introduced to adaptively aggregate low- and high-frequency signals from its neighbors to assimilate/discriminate nodes with assortative/disassortative edges. Then, the label-aware node and edge samplers are designed to remedy graph imbalance problems. Furthermore, the sampled edges are treated as the auxiliary supervision signal to explicitly facilitate the training of graph filters. Extensive experiments on real-world datasets verify the superiority of our method.