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.

欺诈检测在发现欺诈者通过欺骗其他用户，例如发布虚假评论或进行异常交易的行为上有着重要作用。我们采用图神经网络和动态关系注意力聚合机制来解决这个问题，并在真实基准数据集上展示了我们的方法 DRAG 优于其他最先进的欺诈检测方法。