Fraud Detection with Binding Global and Local Relational Interaction
Haolin Li Shuyang Jiang Lifeng Zhang Siyuan Du Guangnan Ye Hongfeng Chai
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.