Symbol table.

This study proposes an advanced Internet fraud transaction detection method, the Temporal-aware Heterogeneous Graph Oversampling and Attention Fusion Network (THG-OAFN), designed to address the increasingly severe fraud issues in EC. The method innovatively abstracts transaction data into a heterogeneous graph structure, captures temporal dynamic features through Gated Recurrent Unit (GRU), and fuses Graph Neural Network (GNN) to process static topological relationships. To address data imbalance, an improved Graph-based Synthetic Minority Oversampling Technique (GraphSMOTE) framework is introduced, maintaining the structural integrity of fraud clusters through k-hop topological constraints. Meanwhile, a multi-layer attention mechanism (including relationship fusion, neighborhood fusion, and information perception modules) is employed to achieve active fraud prevention. Experimental results show that THG-OAFN attains an area under the curve (AUC) of 96.56% (a 7.78% improvement over the best baseline). Moreover, it achieves a recall of 95.21% (a 6.29% improvement) and an F1-score of 94.72% (a 3.96% improvement) on the Amazon dataset. On the YelpChi dataset, these three metrics reach 90.43%, 89.51%, and 90.31%, respectively, remarkably outperforming existing GNN models. This achievement provides a deployable solution for dynamic fraud detection and active defense. Our code is available at https://github.com/wei4zheng/THG-OAFN.本研究提出了一种先进的互联网欺诈交易检测方法——时序感知异构图过采样与注意力融合网络（Temporal-aware Heterogeneous Graph Oversampling and Attention Fusion Network，THG-OAFN），旨在解决电子商务（Electronic Commerce，EC）领域日益严峻的欺诈问题。该方法创新性地将交易数据抽象为异构图结构，通过门控循环单元（Gated Recurrent Unit，GRU）捕捉时序动态特征，并融合图神经网络（Graph Neural Network，GNN）以处理静态拓扑关系。针对数据类别不平衡问题，本研究引入改进的基于图的合成少数类过采样技术（Graph-based Synthetic Minority Oversampling Technique，GraphSMOTE）框架，通过k跳拓扑约束维持欺诈集群的结构完整性。与此同时，本研究采用多层注意力机制（涵盖关系融合、邻域融合与信息感知模块）以实现主动欺诈防控。实验结果表明，THG-OAFN的曲线下面积（Area Under the Curve，AUC）可达96.56%，较最优基线模型提升7.78个百分点。此外，在亚马逊（Amazon）数据集上，该方法的召回率达95.21%（提升6.29个百分点），F1值为94.72%（提升3.96个百分点）。在YelpChi数据集上，上述三项指标分别达到90.43%、89.51%与90.31%，显著优于现有图神经网络模型。该研究成果为动态欺诈检测与主动防御提供了可落地的解决方案。本研究的代码已开源至https://github.com/wei4zheng/THG-OAFN。