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arXiv 2012.108312020-12-20cs.LGcs.CRcs.SI

Suspicious Massive Registration Detection via Dynamic Heterogeneous Graph Neural Networks

KOF Swiss Economic Institute（KOF瑞士经济研究所）；ETH Zurich（苏黎世联邦理工学院）；eBay China（易趣中国）

Susie Xi Rao, Shuai Zhang, Zhichao Han, Zitao Zhang, Wei Min, Mo Cheng, Yinan Shan, Yang Zhao, Ce Zhang

备注：: 8 pages, 1 figure, accepted in the AAAI Workshop on Deep Learning on Graphs 2021

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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.

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