“…Models performing poorly on the minority may lead to undesirable results, as people are more concerned about the minority classes, i.e., the fraudsters. The class imbalance problem on feature-based neural methods has been studied in depth, such as re-sampling, 162 , 163 , 164 re-weighting, 165 , 166 , 167 , 168 and transfer learning. 95 , 169 Whereas in the GNNs works, the noisy information, few interactions among fraudsters, and desalination of the minority class’s features caused by the message aggregation of GNNs are three major challenges in designing class imbalanced GNNs for fraud detection.…”