Abstract
Developed online social networks are recently being grown and popularized tremendously, influencing some life aspects of human. Therefore, privacy preservation is considered as an essential and crucial issue in sharing and propagation of information. There are several methods for privacy preservation in social networks such as limiting the information through community detection. Despite several algorithms proposed so far to detect the communities, numerous researches are still on the way in this area. In this paper, a novel method for community detection with the assumption of privacy preservation is proposed. In the proposed approach is like hierarchical clustering, nodes are divided alliteratively based on learning automata (LA). A set of LA can find min-cut of a graph as two communities for each iteration. Simulation results on standard datasets of social network have revealed a relative improvement in comparison with alternative methods.
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Amiri, F., Yazdani, N., Faili, H., Rezvanian, A. (2013). A Novel Community Detection Algorithm for Privacy Preservation in Social Networks. In: Abraham, A., Thampi, S. (eds) Intelligent Informatics. Advances in Intelligent Systems and Computing, vol 182. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32063-7_47
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DOI: https://doi.org/10.1007/978-3-642-32063-7_47
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