GE-GNN: Gated Edge-Augmented Graph Neural Network for Fraud Detection

Abstract

Graph Neural Networks (GNNs) play a significant role and have been widely applied in fraud detection tasks, exhibiting substantial improvements in detection performance compared to conventional methodologies. However, within the intricate structure of fraud graphs, fraudsters usually camouflage themselves among a large number of benign entities. An effective solution to address the camouflage problem involves the incorporation of complex and abundant edge information. Nevertheless, existing GNN-based methods frequently neglect to integrate this crucial information into the message passing process, thereby limiting their efficacy. To address the above issues, this study proposes a novel Gated Edge-augmented Graph Neural Network(GE-GNN). Our approach begins with an edge-based feature augmentation mechanism that leverages both node and edge features within a single relation. Subsequently, we apply the augmented representation to the message passing process to update the node embeddings. Furthermore, we design a gate logistic to regulate the expression of augmented information. Finally, we integrate node features across different relations to obtain a comprehensive representation. Extensive experimental results on two real-world datasets demonstrate that the proposed method outperforms several state-of-the-art methods.