Nowadays, Multi-purpose Messaging Mobile App (MMMA) has become increasingly
prevalent. MMMAs attract fraudsters and some cybercriminals provide support for
frauds via black market accounts (BMAs). Compared to fraudsters, BMAs are not
directly involved in frauds and are more difficult to detect. This paper
illustrates our BMA detection system SGRL (Self-supervised Graph Representation
Learning) used in WeChat, a representative MMMA with over a billion users. We
tailor Graph Neural Network and Graph Self-supervised Learning in SGRL for BMA
detection. The workflow of SGRL contains a pretraining phase that utilizes
structural information, node attribute information and available human
knowledge, and a lightweight detection phase. In offline experiments, SGRL
outperforms state-of-the-art methods by 16.06%-58.17% on offline evaluation
measures. We deploy SGRL in the online environment to detect BMAs on the
billion-scale WeChat graph, and it exceeds the alternative by 7.27% on the
online evaluation measure. In conclusion, SGRL can alleviate label reliance,
generalize well to unseen data, and effectively detect BMAs in WeChat.

本文提出了一种基于 Graph Neural Network 与 Graph Self-supervised Learning 的黑市账户检测系统 SGRL，用于可以代表超过十亿用户的典型多用途消息移动应用程序 WeChat，SGRL 可以减轻标签的依赖，可以很好地推广到未知数据，并可以有效地检测 WeChat 环境中的黑市账户。