Graph-based Fake Account Detection: A Survey
It addresses the problem of fake account detection for online social network security, but as a survey, it is incremental in summarizing existing work.
This survey reviews graph-based techniques for detecting fake accounts in online social networks, categorizing methods by techniques, input data, and detection time, and discussing datasets and future research directions.
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.