Abstract: In the scenario of credit payment service,the detection of fraudulent users has always been a research hotspot.In the deep learning method,heterogeneous information networks are usually used to model different types of node objects and their interaction relations.For example,nodes are used to represent users and merchants in the payment service scenario,and edges are used to represent the interaction relations between nodes,so as to make full use of the structural information of the graph.However,when capturing node feature information,many models that have been proposed often only focus on the end nodes of the meta path and ignore the information of the middle nodes of the meta path,which will lead to the problem of information loss.Therefore,this paper proposes a heterogeneous graph fraud user detection model based on knowledge graph embedding.Firstly,it introduces the knowledge graph embedding method as the meta path internal aggregation encoder.Different from the method of only focusing on the upper nodes of the meta path,the meta path internal aggregation coder will pay attention to the intermediate nodes of the meta path when obtaining the node information,so as to gather the node information on the whole meta path,which can effectively solve the problem of information loss.Moreover,it designs a multi-layer fusion attention mechanism to simulate users’ preferences for attributes and meta paths from the node and path levels,and analyzes the importance of features from the perspective of fusion at the global level.The experimental results on different types of data sets show that the proposed model achieves relatively good results compared with many existing fraud detection methods.

Key words: Fraud detection, Graph neural network, Heterogeneous graph, Knowledge map embedding, Multi-layer fusion attention mechanism