Generative Graph Augmentation for Minority Class in Fraud Detection

Class imbalance is a well-recognized challenge in GNN-based fraud detection. Traditional methods like re-sampling and re-weighting address this issue by balancing class distribution. However, node class balancing with simple re-sampling or re-weighting may greatly distort the data distributions and eventually lead to the ineffective performance of GNNs. In this paper, we propose a novel approach named Graph Generative Node Augmentation (GGA), which improves GNN-based fraud detection models by augmenting synthetic nodes of the minority class. GGA utilizes the GAN framework to synthesize node features and related edges of fake fraudulent nodes. To introduce greater variety in the generated nodes, we employ an MLP for feature generation. We also introduce an attention module to encode feature-level information before graph convolutional layers for edge generation. Our empirical results on two real-world fraud datasets demonstrate that GGA improves the performance of GNN-based fraud detection models by a large margin with much fewer nodes than traditional class balance methods, and outperforms recent graph augmentation methods with the same number of synthetic nodes.