RWK-GNN:基于特征增强与子核分解的非平衡图欺诈检测算法

RWK-GNN:Fraud Detection for Imbalanced Graphs with Feature Enhancement and Subkernel Decomposition

金融欺诈对经济和社会稳定造成了严重的威胁,因此开发有效的欺诈检测算法对于保护金融系统的完整性至关重要.目前已有多种基于图学习的欺诈检测算法应用于实际场景当中,这些方法或针对图的结构信息开展分类,或通过图卷积神经网络学习节点的嵌入式表示进行欺诈检测工作,关注角度相对单一,无法完备地在非平衡多关系图上开展欺诈检测分析.针对以上问题,本论文提出了一种结合随机游走下的特征增强与子核分解的图神经网络欺诈检测算法(Random Walk feature enhancement and Kcore subkernel decomposition Graph Neural Network,RWKGNN),该算法能够高效地挖掘出多关系不平衡图中节点层级与全局网络层级的拓扑信息,并通过子核分解算法优化图结构特征在社区演进角度上的传播与聚合,最终完成欺诈检测与识别.为验证RWK-GNN算法性能,本文使用了图神经网络欺诈检测任务常用的公开数据集进行模型训练与测试.实验结果表明,在同一评价指标下,该方法较相关机器学习算法与图神经网络算法有着较大提升,与CARE-GNN算法相比,该方法的AUC值提升了17%;与PC-GNN算法相比,该方法的AUC值提升了8%;与SIGN算法相比,该方法的AUC值提升了7%.

Financial fraud poses a serious threat to the economic and social stability,making the development of effective fraud detection algorithms crucial for safeguarding the integrity of the financial system.Currently,various graphbased fraud detection algorithms have been applied in practical scenarios.These methods either classify based on the structural information of graphs or utilize graph convolutional neural networks to learn embedded representations of nodes for fraud detection.However,these approaches have relatively narrow perspectives and cannot comprehensively analyze fraud detection on imbalanced multi-relational graphs.To address these issues,this paper proposes a RWK-GNN(Random Walk feature enhancement and Kcore subkernel decomposition Graph Neural Network),which efficiently extracts topological information at both the node level and the global network level in imbalanced graphs with multiple relationships.It optimizes the propagation and aggregation of graph structural features from the perspective of community evolution through subkernel decomposition algorithm,ultimately achieving fraud detection and identification.To validate the performance of the RWKGNN algorithm,this study employs commonly used public datasets for graph neural network fraud detection tasks in model training and testing.Experimental results demonstrate significant improvements of this method over other machine learning algorithms and graph neural network algorithms in terms of the same evaluation metrics.Compared to the CARE-GNN algorithm,the proposed method achieves a 17%increase in AUC value.Compared to the PC-GNN algorithm,the proposed method achieves an 8%increase in AUC value.Moreover,compared to the SIGN algorithm,the proposed method achieves a 7%increase in AUC value.