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

Abstract

Graph Neural Networks(GNNs) play a significant role and widely applied in fraud detection tasks, exhibiting significant advancements 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. However, existing GNNbased methods often overlook the integration of such 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 utilizes both node and edge features within a single relation. Subsequently, we apply 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 fuse the node features across different relations to obtain a comprehensive representation. Extensive experimental results on two real-world datasets demonstrate the proposed method achieves higher performance over several state-of-the-art methods. Our code is available at https://github.com/shaieesss/GE-GNN

Topics & Concepts