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Community detection in networks by using multiobjective evolutionary algorithm with decomposition

Author

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  • Gong, Maoguo
  • Ma, Lijia
  • Zhang, Qingfu
  • Jiao, Licheng

Abstract

Community structure is an important property of complex networks. Most optimization-based community detection algorithms employ single optimization criteria. In this study, the community detection is solved as a multiobjective optimization problem by using the multiobjective evolutionary algorithm based on decomposition. The proposed algorithm maximizes the density of internal degrees, and minimizes the density of external degrees simultaneously. It can produce a set of solutions which can represent various divisions to the networks at different hierarchical levels. The number of communities is automatically determined by the non-dominated individuals resulting from our algorithm. Experiments on both synthetic and real-world network datasets verify that our algorithm is highly efficient at discovering quality community structure.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:phsmap:v:391:y:2012:i:15:p:4050-4060
    DOI: 10.1016/j.physa.2012.03.021
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    References listed on IDEAS

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    1. Chen, Duanbing & Fu, Yan & Shang, Mingsheng, 2009. "A fast and efficient heuristic algorithm for detecting community structures in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(13), pages 2741-2749.
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    Cited by:

    1. Shang, Ronghua & Luo, Shuang & Zhang, Weitong & Stolkin, Rustam & Jiao, Licheng, 2016. "A multiobjective evolutionary algorithm to find community structures based on affinity propagation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 453(C), pages 203-227.
    2. Karimi, Fatemeh & Lotfi, Shahriar & Izadkhah, Habib, 2021. "Community-guided link prediction in multiplex networks," Journal of Informetrics, Elsevier, vol. 15(4).
    3. Shen, Yi, 2014. "The similarity of weights on edges and discovering of community structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 393(C), pages 560-570.
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    5. Shang, Ronghua & Zhang, Weitong & Jiao, Licheng & Stolkin, Rustam & Xue, Yu, 2017. "A community integration strategy based on an improved modularity density increment for large-scale networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 471-485.
    6. Ebrahimi, Morteza & Shahmoradi, Mohammad Reza & Heshmati, Zainabolhoda & Salehi, Mostafa, 2018. "A novel method for overlapping community detection using Multi-objective optimization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 825-835.
    7. 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.
    8. Manuel Guerrero & Consolación Gil & Francisco G. Montoya & Alfredo Alcayde & Raúl Baños, 2020. "Multi-Objective Evolutionary Algorithms to Find Community Structures in Large Networks," Mathematics, MDPI, vol. 8(11), pages 1-18, November.
    9. Xiao, Jing & Zhang, Yong-Jian & Xu, Xiao-Ke, 2018. "Convergence improvement of differential evolution for community detection in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 762-779.
    10. Wenxin Zhu & Huan Li & Wenhong Wei, 2023. "A Two-Stage Multi-Objective Evolutionary Algorithm for Community Detection in Complex Networks," Mathematics, MDPI, vol. 11(12), pages 1-13, June.
    11. 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.
    12. 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.
    13. Shang, Ronghua & Liu, Huan & Jiao, Licheng, 2017. "Multi-objective clustering technique based on k-nodes update policy and similarity matrix for mining communities in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 1-24.
    14. Fu, Yu-Hsiang & Huang, Chung-Yuan & Sun, Chuen-Tsai, 2016. "Using a two-phase evolutionary framework to select multiple network spreaders based on community structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 840-853.
    15. 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.
    16. Li, Jun-fang & Zhang, Bu-han & Liu, Yi-fang & Wang, Kui & Wu, Xiao-shan, 2012. "Spatial evolution character of multi-objective evolutionary algorithm based on self-organized criticality theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(22), pages 5490-5499.
    17. Lu Wei & Na Liu & Junhua Chen & Jihong Sun, 2022. "Topic Evolution of Chinese COVID-19 Policies Based on Co-Occurrence Clustering Network Analysis," Sustainability, MDPI, vol. 14(4), pages 1-21, February.

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