Fraud Detection with Binding Global and Local Relational Interaction

Graph Neural Network has been proved to be effective for fraud detection for its capability to encode node interaction and aggregate features in a holistic view. Recently, Transformer network with great sequence encoding ability, has also outperformed other GNN-based methods in literatures. However, both GNN-based and Transformer-based networks only encode one perspective of the whole graph, while GNN encodes global features and Transformer network encodes local ones. Furthermore, previous works ignored encoding global interaction features of the heterogeneous graph with separate networks, thus leading to suboptimal performance. In this work, we present a novel framework called Relation-Aware GNN with transFormer (RAGFormer) which simultaneously embeds local and global features into a target node. The simple yet effective network applies a modified GAGA module where each transformer layer is followed by a cross-relation aggregation layer, to encode local embeddings and node interactions across different relations. Apart from the Transformer-based network, we further introduce a Relation-Aware GNN module to learn global embeddings, which is later merged into the local embeddings by an attention fusion module and a skip connection. Extensive experiments on two popular public datasets and an industrial dataset demonstrate that RAGFormer achieves the state-of-the-art performance. Substantial analysis experiments validate the effectiveness of each submodule of RAGFormer and its high efficiency in utilizing small-scale data and low hyper-parameter sensitivity.