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例如发布虚假评论或进行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一个二分类问题：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（GNNs）来解决这一问题。基于许多现实世界图包含不同类型关系的观察，我们提出为每种关系学习一个节点表示，并使用一个可学习的注意力函数来聚合这些节点表示，该函数为每种关系分配不同的注意力系数。此外，我们结合来自不同层的节点表示，以同时考虑目标节点的局部和全局结构，这有助于提升在具有异质性图上的欺诈检测性能。通过在所有聚合过程中采用动态图注意力，我们的方法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在现实世界基准数据集上优于最先进的欺诈检测方法。