综述图神经网络在社交平台假账号识别中的应用
Graph-based Fake Account Detection: A Survey
- 基于社交关系图拓扑结构识别假账号
- 涵盖多种技术路线与数据集对比分析
- 适合关注安全检测与图学习的研究者
近年来，针对在线社交网络中假账号检测的有效且高效算法研究日益增多。本文系统综述了现有方法，重点聚焦于利用社交图拓扑特征（除账号信息如共享内容和资料数据外）区分真实与虚假账号的图基技术。我们从技术手段、输入数据、检测时间等多个维度对这些方法进行分类，分析其优劣，并阐述它们在整体研究背景中的关联。同时，考察了现有可用数据集，包括真实世界数据与合成模型。最后，提出若干未来研究方向。
原文摘要 · Abstract (English)
In recent years, there has been a growing effort to develop effective and efficient algorithms for fake account detection in online social networks. This survey comprehensively reviews existing methods, with a focus on graph-based techniques that utilise topological features of social graphs (in addition to account information, such as their shared contents and profile data) to distinguish between fake and real accounts. We provide several categorisations of these methods (for example, based on techniques used, input data, and detection time), discuss their strengths and limitations, and explain how these methods connect in the broader context. We also investigate the available datasets, including both real-world data and synthesised models. We conclude the paper by proposing several potential avenues for future research.
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