At online retail platforms, detecting fraudulent accounts and transactions is crucial to improve customer experience, minimize loss, and avoid unauthorized transactions. Despite the variety of different models for deep learning on graphs, few approaches have been proposed for dealing with graphs that are both heterogeneous and dynamic. In this paper, we propose DyHGN (Dynamic Heterogeneous Graph Neural Network) and its variants to capture both temporal and heterogeneous information. We first construct dynamic heterogeneous graphs from registration and transaction data from eBay. Then, we build models with diachronic entity embedding and heterogeneous graph transformer. We also use model explainability techniques to understand the behaviors of DyHGN-* models. Our findings reveal that modelling graph dynamics with heterogeneous inputs need to be conducted with "attention" depending on the data structure, distribution, and computation cost.
翻译:在网上零售平台上,发现欺诈账户和交易对于改善客户经验、尽量减少损失和避免未经授权的交易至关重要。尽管在图表上有各种不同的深入学习模式,但很少提议采用不同和动态的图表处理方法。在本文中,我们提议DyHGN(动态超异形图形神经网络)及其变体,以捕捉时间和不同的信息。我们首先从eBay的登记和交易数据中构建动态多变图表。然后,我们用对称实体嵌入和多元图形变压器来构建模型。我们还使用模型解释技术来理解DyHGN-* 模型的行为。我们的调查结果显示,根据数据结构、分布和计算成本,需要“注意”使用不同投入的模型图形动态。