Graph-based Fake Account Detection: A Survey

TL;DR

This survey reviews graph-based methods for detecting fake accounts in social networks, analyzing their techniques, datasets, and limitations, and suggests future research directions.

Contribution

It provides a comprehensive categorization and analysis of existing graph-based fake account detection methods, highlighting their strengths and gaps.

Findings

Graph-based techniques effectively identify fake accounts.

Various datasets, including real and synthetic, are used for evaluation.

Future research should focus on improving detection accuracy and scalability.

Abstract

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.