Graph-based Fake Account Detection: A Survey
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912472902926336 |
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| author | Dehkordi, Ali Safarpoor Zehmakan, Ahad N. |
| author_facet | Dehkordi, Ali Safarpoor Zehmakan, Ahad N. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_06541 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Graph-based Fake Account Detection: A Survey Dehkordi, Ali Safarpoor Zehmakan, Ahad N. Social and Information Networks Artificial Intelligence Machine Learning A.1; I.2.6; I.5.1 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. |
| title | Graph-based Fake Account Detection: A Survey |
| topic | Social and Information Networks Artificial Intelligence Machine Learning A.1; I.2.6; I.5.1 |
| url | https://arxiv.org/abs/2507.06541 |