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A Hybrid Bat Algorithm for Community Detection in Social Networks

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Intelligent Systems Design and Applications (ISDA 2018 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 941))

Abstract

In this work, a hybrid optimization method is proposed for dealing with the community discovery problem in social networks relying on the bat algorithm. The proposed method hybrids discrete bat algorithm with Tabu search for enhancing the quality of solution in contrast to discrete bat algorithm. The Tabu search is a neighborhood search based method. The local search capability of bat algorithm is improved by introducing the Tabu search strategy. The recommended hybrid approach is tested on a few real-world networks and synthetic benchmark network. The obtained results are very promising and comparable as well. The results are compared with existing algorithms which demonstrate that the proposed method enhances the quality of the obtained solution.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Jamia Millia Islamia, New Delhi, India

    Seema Rani & Monica Mehrotra

Authors
  1. Seema Rani
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  2. Monica Mehrotra
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Corresponding author

Correspondence to Seema Rani .

Editor information

Editors and Affiliations

  1. Machine Intelligence Research Labs, Auburn, WA, USA

    Ajith Abraham

  2. School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

    Aswani Kumar Cherukuri

  3. Tijuana Institute of Technology, Tijuana, Mexico

    Patricia Melin

  4. Machine Intelligence Research Labs, Auburn, WA, USA

    Niketa Gandhi

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Rani, S., Mehrotra, M. (2020). A Hybrid Bat Algorithm for Community Detection in Social Networks. In: Abraham, A., Cherukuri, A., Melin, P., Gandhi, N. (eds) Intelligent Systems Design and Applications. ISDA 2018 2018. Advances in Intelligent Systems and Computing, vol 941. Springer, Cham. https://doi.org/10.1007/978-3-030-16660-1_92

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