论文标题
用“注意”建模欺诈检测中的图形动力学
Modelling graph dynamics in fraud detection with "Attention"
论文作者
论文摘要
在在线零售平台上，检测欺诈性帐户和交易对于改善客户体验，最大程度地减少损失并避免未经授权的交易至关重要。尽管有各种各样的图形深度学习模型，但很少有人提出处理既有异质又动态的图形的方法。在本文中，我们提出了Dyhgn（动态异质图神经网络）及其变体以捕获时间和异质信息。我们首先从eBay的注册和交易数据中构造动态异构图。然后，我们使用历时性实体嵌入和异质图变压器构建模型。我们还使用模型解释性技术来了解Dyhgn-*模型的行为。我们的发现表明，根据数据结构，分布和计算成本，需要以“注意”进行模拟图形动力学以“注意”进行。
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.
