Fraud detection aims to discover fraudsters deceiving other users by, for example, leaving fake reviews or making abnormal transactions. Graph-based fraud detection methods consider this task as a classification problem with two classes: frauds or normal. We address this problem using Graph Neural Networks (GNNs) by proposing a dynamic relation-attentive aggregation mechanism. Based on the observation that many real-world graphs include different types of relations, we propose to learn a node representation per relation and aggregate the node representations using a learnable attention function that assigns a different attention coefficient to each relation. Furthermore, we combine the node representations from different layers to consider both the local and global structures of a target node, which is beneficial to improving the performance of fraud detection on graphs with heterophily. By employing dynamic graph attention in all the aggregation processes, our method adaptively computes the attention coefficients for each node. Experimental results show that our method, DRAG, outperforms state-of-the-art fraud detection methods on real-world benchmark datasets.

翻译：欺诈检测旨在发现通过虚假评论或异常交易等方式欺骗其他用户的欺诈者。基于图的欺诈检测方法将此任务视为二分类问题：欺诈或正常。我们通过提出一种动态关系注意聚合机制，利用图神经网络解决该问题。基于真实世界图通常包含多种关系类型的观察，我们提出为每种关系学习节点表示，并使用可学习注意力函数聚合节点表示，该函数为每种关系赋予不同注意力系数。此外，我们融合不同层级的节点表示，综合考虑目标节点的局部与全局结构，这有助于提升异构图上欺诈检测的性能。通过在全部聚合过程中采用动态图注意力机制，我们的方法能够自适应地为每个节点计算注意力系数。实验结果表明，我们的方法DRAG在真实世界基准数据集上优于最先进的欺诈检测方法。