  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.
