基于噪声过滤与特征增强的图神经网络欺诈检测方法

Noise Filtering and Feature Enhancement Based Graph Neural Network Method for Fraud Detection

现有的基于图神经网络(Graph Neural Network,GNN)的欺诈检测方法还存在三个方面的不足:(1)没有充分考虑到样本标签分布不平衡的问题;(2)没有考虑欺诈者为了躲避检测器的检测,故意制造噪声干扰检测的问题;(3)没有考虑欺诈类型数据联系稀疏问题.为此,本文提出一种基于噪声过滤与特征增强的图神经网络欺诈检测方法NFE-GNN(Noise Filtering and feature Enhancement based Graph Neural Network method for fraud detection)来改善欺诈检测性能.该方法首先基于数据集的欺诈率对样本进行平衡采样;在此基础上,采用一个参数化距离函数计算节点间的相似度,并通过强化学习得到最优的噪声过滤阈值;最后,通过创建欺诈样本间的联系,丰富拓扑信息,以达到增强欺诈类特征嵌入表示的目的.在两个公开数据集上的实验结果表明,本文所提NFE-GNN方法的性能优于目前主流的图神经网络欺诈检测方法.

Existing graph neural network(GNN)-based fraud detection methods have at least three shortcomings:(1)They do not adequately consider the problem of imbalanced distribution of sample labels.(2)They do not take into account the problem that fraudsters deliberately create noise to interfere with fraud detection in order to avoid detection by detectors.(3)They fail to consider the limitations of sparse connections for fraud data.To address these three shortcomings,this paper proposes a fraud detection method,called NFE-GNN(Noise Filtering and feature Enhancement based Graph Neural Network method for fraud detection),to improve the fraud detection performance.The proposed NFE-GNN method first employs a dataset-based fraud rate sampling technology to achieve a balance of benign and fraudulent samples.Based on this,a parameterized distance function is introduced to calculate the similarities between nodes,and the optimal noise filtering threshold is obtained through adaptive reinforcement learning.Finally,an effective algorithm is presented to increase the connections between fraudulent samples,and enrich the topology information in the graph to enhance the feature representation capability of fraudulent samples.The experimental results on two publicly available datasets demonstrate that the detection performance of the proposed NFE-GNN method is better than that of state-of-the-art graph neural network methods.