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A Nature-Inspired Metaheuristic Cuckoo Search Algorithm for Community Detection in Social Networks

Author

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  • Ramadan Babers

    (Helwan University, Helwan, Egypt, and Scientific Research Group in Egypt (SRGE), Egypt)

  • Aboul Ella Hassanien

    (Cairo University, Giza, Egypt, and Scientific Research Group in Egypt (SRGE), Egypt)

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.

Suggested Citation

  • Ramadan Babers & Aboul Ella Hassanien, 2017. "A Nature-Inspired Metaheuristic Cuckoo Search Algorithm for Community Detection in Social Networks," International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), IGI Global, vol. 8(1), pages 50-62, January.
    • Handle: RePEc:igg:jssmet:v:8:y:2017:i:1:p:50-62
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    Cited by:

    1. Dhuha Abdulhadi Abduljabbar & Siti Zaiton Mohd Hashim & Roselina Sallehuddin, 2020. "Nature-inspired optimization algorithms for community detection in complex networks: a review and future trends," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 74(2), pages 225-252, June.

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