PDF(Pubmed)
Abstract：
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.
摘要：
诈骗行为严重威胁个人利益和社会稳定,因此，欺诈检测近年来备受关注。在社交媒体等场景中，欺诈者通常隐藏在众多良性用户中，只构成一小部分，经常形成“小帮派”。由于欺诈者的稀缺，传统的图神经网络可能会忽略或掩盖关键的欺诈信息，导致欺诈特征代表性不足。为了解决这些问题,本研究提出了图上的tran-smote(GTS)欺诈检测方法。利用子图神经网络提取器深度挖掘各类节点的结构特征,使用变压器技术将这些功能与属性功能集成在一起，丰富了节点的信息表示，从而解决了特征表示不足的问题。此外,这种方法涉及设置一个特征嵌入空间来生成代表少数类的新节点，边缘生成器用于为这些新节点提供相关的连接信息，缓解阶级不平衡问题。在两个真实数据集上的实验结果表明，所提出的GTS，性能优于当前最先进的基线。
