Wen Jintao, Tang Xianghong, Lu Jianguang
College of Computer Science and Technology, Guizhou University, Guiyang, 550025, China.
State Key Laboratory of Public Big Data, Guizhou University, Guiyang, 550025, China.
Sci Rep. 2024 Jul 17;14(1):16560. doi: 10.1038/s41598-024-67550-4.
Fraud seriously threatens individual interests and social stability, so fraud detection has attracted much attention in recent years. In scenarios such as social media, fraudsters typically hide among numerous benign users, constituting only a small minority and often forming "small gangs". Due to the scarcity of fraudsters, the conventional graph neural network might overlook or obscure critical fraud information, leading to insufficient representation of fraud characteristics. To address these issues, the tran-smote on graphs (GTS) method for fraud detection is proposed by this study. Structural features of each type of node are deeply mined using a subgraph neural network extractor, these features are integrated with attribute features using transformer technology, and the node's information representation is enriched, thereby addressing the issue of inadequate feature representation. Additionally, this approach involves setting a feature embedding space to generate new nodes representing minority classes, and an edge generator is used to provide relevant connection information for these new nodes, alleviating the class imbalance problem. The results from experiments on two real datasets demonstrate that the proposed GTS, performs better than the current state-of-the-art baseline.
欺诈严重威胁个人利益和社会稳定，因此近年来欺诈检测备受关注。在社交媒体等场景中，欺诈者通常隐藏在众多良性用户之中，仅占少数，且常常形成“小团伙”。由于欺诈者数量稀少，传统的图神经网络可能会忽略或模糊关键的欺诈信息，导致欺诈特征表示不足。为解决这些问题，本研究提出了用于欺诈检测的图上迁移过采样技术（GTS）方法。使用子图神经网络提取器深入挖掘每种类型节点的结构特征，利用Transformer技术将这些特征与属性特征集成，丰富节点的信息表示，从而解决特征表示不足的问题。此外，该方法通过设置特征嵌入空间来生成代表少数类别的新节点，并使用边生成器为这些新节点提供相关连接信息，缓解类不平衡问题。在两个真实数据集上的实验结果表明，所提出的GTS方法比当前最先进的基线方法表现更好。
