On Using Node Indices and Their Correlations for Fake Account Detection
Paper in proceeding, 2022

With the growing rate of online social networks, the number of fake accounts is multiplying day by day. There exist many approaches in the literature that try to distinguish fake accounts from real ones, for example, those that use machine learning and classification techniques to learn whether a user should be labeled as fake (bot) or not. In this paper, we follow a different approach and try to use node measurements in the field of complex networks analysis to identify fake accounts. We first model users' interactions with a large graph. For example, in Twitter, we can form graphs of follower-following, comments, retweets, mentions, and so on. We then investigate different measurements, such as centrality indices and their correlations, to separate real and fake accounts. We find that measurements such as average path length, eigenvector centrality, harmonic centrality, degree, local reaching centrality and their correlations provide good indicators to distinguish real and fake accounts.

Online social networks

centrality measures


fake account detection

complex networks analysis


Sara Asghari

Amirkabir University of Technology

Mostafa Haghir Chehreghani

Amirkabir University of Technology

Morteza Haghir Chehreghani

Chalmers, Computer Science and Engineering (Chalmers), Data Science and AI

Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022

9781665480451 (ISBN)

2022 IEEE International Conference on Big Data, Big Data 2022
Osaka, Japan,

Subject Categories

Other Computer and Information Science

Human Aspects of ICT

Communication Systems



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