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An Adaptive Approach for Community Detection Based on Chicken Swarm Optimization Algorithm

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Book cover Genetic and Evolutionary Computing (ICGEC 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 536))

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Abstract

This paper presents an adaptive approach based on chicken swarm optimization algorithm (ACSO) for community detection problem in complex social networks. The proposed approach is able to define dynamically the number of communities for complex social network. The basic chicken swarm algorithm by its nature is continuous which can’t fit for community detection domain so it needs to be redesigned as a discrete chicken swarm for a better exploration of the search space. Locus-based adjacency scheme is used for encoding and decoding tasks while NMI and Modularity are used as an objective function.

The proposed approach is executed over four popular cited benchmarks data sets with different size of small, medium and large scale data sets such as Zachary karate club, Bottlenose dolphin, American college football and Facebook. Experimental results are measured with quality measures such as NMI, Modularity and Ground truth. ACSO’s results are compared with eight well-known community detection algorithms such as A discrete BAT, Artificial fish swarm, Infomap, Fast Greedy, label propagation, Walktrap, Multilevel and A discrete Krill herd Algorithm. ACSO has achieved high accuracy and quality results for community detection and community structure for complex social networks.

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Author information

Authors and Affiliations

  1. Faculty of Computers and Information, Cairo University, Giza, Egypt

    Khaled Ahmed, Aboul Ella Hassanien & Ehab Ezzat

  2. College of Information Science and Engineering, Fujian University of Technology, Fuzhou, China

    Pei-Wei Tsai

  3. Scientific Research Group in Egypt (SRGE), Cairo, Egypt

    Khaled Ahmed & Aboul Ella Hassanien

Authors
  1. Khaled Ahmed
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  2. Aboul Ella Hassanien
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  3. Ehab Ezzat
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  4. Pei-Wei Tsai
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Corresponding author

Correspondence to Khaled Ahmed .

Editor information

Editors and Affiliations

  1. College of Information Science and Engineering, Fujian University of Technology, Fuzhou, Fujian, China

    Jeng-Shyang Pan

  2. School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong, China

    Jerry Chun-Wei Lin

  3. College of Information Science and Engineering, Fujian University of Technology, Fuzhou, Fujian, China

    Chia-Hung Wang

  4. College of Information Science and Engineering, Fujian University of Technology, Fuzhou, Fujian, China

    Xin Hua Jiang

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Ahmed, K., Hassanien, A.E., Ezzat, E., Tsai, PW. (2017). An Adaptive Approach for Community Detection Based on Chicken Swarm Optimization Algorithm. In: Pan, JS., Lin, JW., Wang, CH., Jiang, X. (eds) Genetic and Evolutionary Computing. ICGEC 2016. Advances in Intelligent Systems and Computing, vol 536. Springer, Cham. https://doi.org/10.1007/978-3-319-48490-7_33

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  • DOI: https://doi.org/10.1007/978-3-319-48490-7_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48489-1

  • Online ISBN: 978-3-319-48490-7

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