Graph neural networks (GNN) have emerged as a powerful tool for fraud detection tasks, where fraudulent nodes are identified by aggregating neighbor information via different relations. To get around such detection, crafty fraudsters resort to camouflage via connecting to legitimate users (i.e., relation camouflage) or providing seemingly legitimate feedbacks (i.e., feature camouflage). A wide-spread solution reinforces the GNN aggregation process with neighbor selectors according to original node features. This method may carry limitations when identifying fraudsters not only with the relation camouflage, but with the feature camouflage making them hard to distinguish from their legitimate neighbors. In this paper, we propose a Hierarchical Attention-based Graph Neural Network (HA-GNN) for fraud detection, which incorporates weighted adjacency matrices across different relations against camouflage. This is motivated in the Relational Density Theory and is exploited for forming a hierarchical attention-based graph neural network. Specifically, we design a relation attention module to reflect the tie strength between two nodes, while a neighborhood attention module to capture the long-range structural affinity associated with the graph. We generate node embeddings by aggregating information from local/long-range structures and original node features. Experiments on three real-world datasets demonstrate the effectiveness of our model over the state-of-the-arts.
翻译:内心图网络(GNN)已成为一个强有力的欺诈检测任务工具,其中欺诈节点是通过通过不同关系汇总邻居信息而发现的。为了绕过这种检测,狡猾的欺诈者通过与合法用户的联系(即关系迷彩)或提供貌似合法的反馈(即功能迷彩)来进行伪装。一个广泛的解决方案加强了GNN聚合过程,根据原始节点特征与邻居选择者一起进行。这种方法在识别欺诈者时可能带有局限性,不仅与关系伪装有关,而且与身份伪装有关,使其难以与合法邻居区分。在本文中,我们建议建立一个基于高度注意的图形神经网络(HA-GNNN),用于检测欺诈,其中结合了不同关系之间的加权对称矩阵(即关系迷彩色迷彩)或提供貌似合法的反馈(即功能迷彩色迷彩色迷彩。这是由关系密度理论推动的,并被用于形成一个基于关注关注程度的图表等级的图形网络。我们设计了一个关系关注模块,以反映两个节点之间的连接力,而社区关注模块则难以辨别它们与合法邻居之间的长期结构亲近关系。我们在原始模型上展示了原始数据结构上的模型上,我们没有定位。我们通过将原始数据定位的原始模型将数据嵌嵌入了世界结构进行定位。