Financial fraud is always a major problem in the field of finance, as it can cause significant consequences. As a result, many approaches have been designed to detect it, and lately Graph Neural Networks (GNNs) have been demonstrated as a competent candidate. However, when trained with a large amount of data, they are slow and computationally demanding. In addition, GNNs may need a deep architecture to detect complex fraud patterns, but doing so may make them suffer from problems such as over-fitting or over-smoothing. Over-fitting leads to reduced generalisation of the model on unseen data, while over-smoothing causes all nodes' features to converge to a fixed point due to excessive aggregation of information from neighbouring nodes. In this research, I propose an approach called One-Side Edge Sampling (OES) that can potentially reduce training duration as well as the effects of over-smoothing and over-fitting. The approach leverages predictive confidence in an edge classification task to sample edges from the input graph during a certain number of epochs. To explain why OES can alleviate over-smoothing, I perform a theoretical analysis of the proposed approach. In addition, to validate the effect of OES, I conduct experiments using different GNNs on two datasets. The results show that OES can empirically outperform backbone models in both shallow and deep architectures while also reducing training time.

翻译：金融欺诈始终是金融领域的一个主要问题，因其可能造成严重后果。因此，人们设计了多种方法来检测欺诈，而近期图神经网络（GNNs）已被证明是一种有效的解决方案。然而，当使用大量数据进行训练时，GNNs存在速度慢、计算需求高的问题。此外，GNNs可能需要深层架构来检测复杂的欺诈模式，但这样做可能导致过拟合或过平滑等问题。过拟合会降低模型在未见数据上的泛化能力，而过平滑则由于过度聚合邻接节点的信息，导致所有节点的特征收敛到一个固定点。在本研究中，我提出了一种称为单边边采样（OES）的方法，该方法有望减少训练时间，并缓解过平滑和过拟合的影响。该方法利用边分类任务中的预测置信度，在特定训练周期内从输入图中采样边。为了解释OES为何能缓解过平滑，我对所提方法进行了理论分析。此外，为验证OES的效果，我在两个数据集上使用不同的GNNs进行了实验。结果表明，OES在浅层和深层架构中均能实证优于基线模型，同时减少了训练时间。