Massive account registration has raised concerns on risk management in e-commerce companies, especially when registration increases rapidly within a short time frame. To monitor these registrations constantly and minimize the potential loss they might incur, detecting massive registration and predicting their riskiness are necessary. In this paper, we propose a Dynamic Heterogeneous Graph Neural Network framework to capture suspicious massive registrations (DHGReg). We first construct a dynamic heterogeneous graph from the registration data, which is composed of a structural subgraph and a temporal subgraph. Then, we design an efficient architecture to predict suspicious/benign accounts. Our proposed model outperforms the baseline models and is computationally efficient in processing a dynamic heterogeneous graph constructed from a real-world dataset. In practice, the DHGReg framework would benefit the detection of suspicious registration behaviors at an early stage.

本文提出一种动态异构图神经网络框架（DHGReg），以检测可疑的大规模注册行为，构建出包含结构子图和时间子图的动态异构图并设计有效的架构来预测可疑/良性账户，该模型的性能优于基准模型，且具有计算效率。在实践中，DHGReg框架将有助于早期发现可疑的注册行为。

基于动态异构图神经网络的可疑大规模注册检测