Forthcoming and Online First Articles

International Journal of Social Network Mining

International Journal of Social Network Mining (IJSNM)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

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International Journal of Social Network Mining (One paper in press)

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  • Community detection using imperialist competitive algorithm   Order a copy of this article
    by Zahra Mahmoodabadi, Abdorreza Savadi 
    Abstract: The clustering of complex networks has become one of the most fascinating fields in recent years. The clustering of complex networks, including social networks, provides useful information. This information leads to optimal structures in complex networks and is very useful for solving real-world problems. Social network graphs have an important feature that random graphs do not have: community structure. The problem of community detection in social network graphs corresponds to graph partitioning and is an NP-hard problem. Therefore, the application of evolutionary algorithms can be an appropriate solution to this problem. This paper uses the imperialist competitive algorithm (ICA) to identify communities within a social network graph. In addition, a parallel version of the ICA was applied to the problem. The results show that the parallel version has better convergence rather than the serial ICA. To compare the performance of ICA with other evolutionary algorithms, we use the PSO algorithm for the community detection problem. The results confirm that parallel ICA (PICA) is superior in both cost value and convergence rate.
    Keywords: parallel ICA; PICA; community detection; social networks; evolutionary algorithms.
    DOI: 10.1504/IJSNM.2023.10059293
     

 

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