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.
Cite
@article{arxiv.2012.10831,
title = {Suspicious Massive Registration Detection via Dynamic Heterogeneous Graph Neural Networks},
author = {Susie Xi Rao and Shuai Zhang and Zhichao Han and Zitao Zhang and Wei Min and Mo Cheng and Yinan Shan and Yang Zhao and Ce Zhang},
journal= {arXiv preprint arXiv:2012.10831},
year = {2020}
}
Comments
8 pages, 1 figure, accepted in the AAAI Workshop on Deep Learning on Graphs 2021