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.

本研究提出了动态异构图神经网络及其变体 DyHGN，使用商业交易数据来构建动态异构图，并使用模型解释技术来理解 DyHGN-* 模型的行为，其发现表明建模带异构输入的图动态行为需要依赖于数据结构、分布和计算成本的 “关注”。