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Nature-inspired optimization algorithms for community detection in complex networks: a review and future trends

Author

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  • Dhuha Abdulhadi Abduljabbar

    (Universiti Teknologi Malaysia (UTM)
    Baghdad University)

  • Siti Zaiton Mohd Hashim

    (Universiti Teknologi Malaysia (UTM))

  • Roselina Sallehuddin

    (Universiti Teknologi Malaysia (UTM))

Abstract

Over the past couple of decades, the research area of network community detection has seen substantial growth in popularity, leading to a wide range of researches in the literature. Nature-inspired optimization algorithms (NIAs) have given a significant contribution to solving the community detection problem by transcending the limitations of other techniques. However, due to the importance of the topic and its prominence in many applications, the information on it is scattered in various journals, conference proceedings, and patents, and lacked a focused-literature that synthesizes them in a single document. This review aims to provide an overview of the NIAs and their role in solving community detection problems. To achieve this goal, a systematic study is performed on NIAs, followed by historical and statistical analysis of the researches involved. This would lead to the identification of future trends, as well as the discovery of related research challenges. This review provides a guide for researchers to identify new areas of research, as well as directing their future interest towards developing more effective frameworks in the context of nature-inspired community detection algorithms.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:telsys:v:74:y:2020:i:2:d:10.1007_s11235-019-00636-x
    DOI: 10.1007/s11235-019-00636-x
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    References listed on IDEAS

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    1. Gong, Maoguo & Ma, Lijia & Zhang, Qingfu & Jiao, Licheng, 2012. "Community detection in networks by using multiobjective evolutionary algorithm with decomposition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(15), pages 4050-4060.
    2. Zhou, Xu & Liu, Yanheng & Zhang, Jindong & Liu, Tuming & Zhang, Di, 2015. "An ant colony based algorithm for overlapping community detection in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 427(C), pages 289-301.
    3. Federico Botta & Charo I del Genio, 2017. "Analysis of the communities of an urban mobile phone network," PLOS ONE, Public Library of Science, vol. 12(3), pages 1-14, March.
    4. Amir Lakizadeh & Saeed Jalili, 2016. "BiCAMWI: A Genetic-Based Biclustering Algorithm for Detecting Dynamic Protein Complexes," PLOS ONE, Public Library of Science, vol. 11(7), pages 1-16, July.
    5. Zhu, Xiaoyu & Ma, Yinghong & Liu, Zhiyuan, 2018. "A novel evolutionary algorithm on communities detection in signed networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 938-946.
    6. 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.
    7. Shang, Ronghua & Bai, Jing & Jiao, Licheng & Jin, Chao, 2013. "Community detection based on modularity and an improved genetic algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(5), pages 1215-1231.
    8. Zou, Feng & Chen, Debao & Huang, De-Shuang & Lu, Renquan & Wang, Xude, 2019. "Inverse modelling-based multi-objective evolutionary algorithm with decomposition for community detection in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 662-674.
    9. Moradi, Mehdi & Parsa, Saeed, 2019. "An evolutionary method for community detection using a novel local search strategy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 457-475.
    10. Jie Zhao & Xiujuan Lei & Fang-Xiang Wu, 2017. "Predicting Protein Complexes in Weighted Dynamic PPI Networks Based on ICSC," Complexity, Hindawi, vol. 2017, pages 1-11, August.
    11. Chuan Shi & Zhenyu Yan & Yi Wang & Yanan Cai & Bin Wu, 2010. "A Genetic Algorithm For Detecting Communities In Large-Scale Complex Networks," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 13(01), pages 3-17.
    12. Li, Zhangtao & Liu, Jing, 2016. "A multi-agent genetic algorithm for community detection in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 449(C), pages 336-347.
    13. Nancy Girdhar & K. K. Bharadwaj, 2019. "Community Detection in Signed Social Networks Using Multiobjective Genetic Algorithm," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 70(8), pages 788-804, August.
    14. Peng Wu & Li Pan, 2015. "Multi-Objective Community Detection Based on Memetic Algorithm," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-31, May.
    15. Firat, Aykut & Chatterjee, Sangit & Yilmaz, Mustafa, 2007. "Genetic clustering of social networks using random walks," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 6285-6294, August.
    16. Dongxiao He & Jie Liu & Bo Yang & Yuxiao Huang & Dayou Liu & Di Jin, 2012. "An Ant-Based Algorithm With Local Optimization For Community Detection In Large-Scale Networks," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 15(08), pages 1-26.
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