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A Nature-Inspired Metaheuristic Cuckoo Search Algorithm for Community Detection in Social Networks
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Author(s): Ramadan Babers (Helwan University, Helwan, Egypt, and Scientific Research Group in Egypt (SRGE), Egypt)and Aboul Ella Hassanien (Cairo University, Giza, Egypt, and Scientific Research Group in Egypt (SRGE), Egypt)
Copyright: 2017
Volume: 8
Issue: 1
Pages: 13
Source title:
International Journal of Service Science, Management, Engineering, and Technology (IJSSMET)
Editor(s)-in-Chief: Ahmad Taher Azar (College of Computer & Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia & Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt)and Ghazy Assassa (Benha University, Egypt)
DOI: 10.4018/IJSSMET.2017010104
PurchaseView on the publisher's website for pricing and purchasing information.
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Abstract
In last few years many approaches have been proposed to detect communities in social networks using diverse ways. Community detection is one of the important researches in social networks and graph analysis. This paper presents a cuckoo search optimization algorithm with Lévy flight for community detection in social networks. Experimental on well-known benchmark data sets demonstrates that the proposed algorithm can define the structure and detect communities of complex networks with high accuracy and quality. In addition, the proposed algorithm is compared with some swarms algorithms including discrete bat algorithm, artificial fish swarm, discrete Krill Herd, ant lion algorithm and lion optimization algorithm and the results show that the proposed algorithm is competitive with these algorithms.
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