Graph-based fraud detection (GFD) can be regarded as a challenging semi-supervised node binary classification task. In recent years, Graph Neural Networks (GNN) have been widely applied to GFD, characterizing the anomalous possibility of a node by aggregating neighbor information. However, fraud graphs are inherently heterophilic, thus most of GNNs perform poorly due to their assumption of homophily. In addition, due to the existence of heterophily and class imbalance problem, the existing models do not fully utilize the precious node label information. To address the above issues, this paper proposes a semi-supervised GNN-based fraud detector SEC-GFD. This detector includes a hybrid filtering module and a local environmental constraint module, the two modules are utilized to solve heterophily and label utilization problem respectively. The first module starts from the perspective of the spectral domain, and solves the heterophily problem to a certain extent. Specifically, it divides the spectrum into various mixed-frequency bands based on the correlation between spectrum energy distribution and heterophily. Then in order to make full use of the node label information, a local environmental constraint module is adaptively designed. The comprehensive experimental results on four real-world fraud detection datasets denote that SEC-GFD outperforms other competitive graph-based fraud detectors. We release our code at https://github.com/Sunxkissed/SEC-GFD.

翻译：图欺诈检测可视为一项具有挑战性的半监督节点二分类任务。近年来，图神经网络被广泛应用于图欺诈检测领域，通过聚合邻居信息来刻画节点的异常可能性。然而，欺诈图本质上具有异质性，因此大多数基于同质性假设的图神经网络在此类场景中表现欠佳。此外，由于异质性与类别不平衡问题的存在，现有模型未能充分利用宝贵的节点标签信息。为解决上述问题，本文提出一种基于图神经网络的半监督欺诈检测器SEC-GFD。该检测器包含混合滤波模块与局部环境约束模块，分别用于解决异质性问题和标签利用问题。第一模块从谱域视角出发，通过分析谱能量分布与异质性的关联性，将频谱划分为多个混合频带，从而在一定程度上缓解异质性问题。为充分利用节点标签信息，本文自适应地设计了局部环境约束模块。在四个真实世界欺诈检测数据集上的综合实验表明，SEC-GFD的性能优于其他先进的图欺诈检测方法。代码已发布于https://github.com/Sunxkissed/SEC-GFD。