基于知识图谱嵌入的异构图欺诈用户检测

doi:10.11896/jsjkx.250400085

摘要/Abstract

摘要： 在信用支付服务场景中,欺诈用户的检测问题一直是一个研究热点。在深度学习方法中,通常使用异质信息网络来建模不同类型的节点对象及其交互关系,如用节点表示支付服务场景中的用户及商家,用边来表示节点之间的交互关系,以充分利用图的结构信息。然而,已经提出的很多模型在捕捉节点特征信息时,往往只关注元路径端节点而忽略了元路径中间节点的信息,这将导致信息丢失的问题。因此,提出了一种基于知识图谱嵌入的异构图欺诈用户检测模型。首先,引入知识图谱嵌入方法作为元路径内部聚合编码器,与只关注元路径上端节点的方法不同,元路径内部聚合编码器在获取节点信息时会同时关注元路径中间节点,以聚集整条元路径上的节点信息,能够有效解决信息丢失的问题。除此之外,设计了一个多层融合注意力机制,从节点以及路径层面模拟用户对属性和元路径的偏好,并在全局层面以融合的角度分析特征的重要程度。在不同类型数据集上的实验结果表明,与现有的多种欺诈检测方法相比,所提模型取得了相对较好的结果。

关键词: 欺诈检测, 图神经网络, 异构图, 知识图谱嵌入, 多层融合注意力机制

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

中图分类号:

TP391

吕舒琦, 张云峰. 基于知识图谱嵌入的异构图欺诈用户检测[J]. 计算机科学, 2025, 52(11A): 250400085-7. https://doi.org/10.11896/jsjkx.250400085

LYU Shuqi, ZHANG Yunfeng. Fraud User Detection Based on Heterogeneous Information Network with Knowledge Graph Eembedding[J]. Computer Science, 2025, 52(11A): 250400085-7. https://doi.org/10.11896/jsjkx.250400085

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