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Multiobjective biogeography based optimization algorithm with decomposition for community detection in dynamic networks

Author

Listed:
  • Zhou, Xu
  • Liu, Yanheng
  • Li, Bin
  • Sun, Geng

Abstract

Identifying community structures in static network misses the opportunity to capture the evolutionary patterns. So community detection in dynamic network has attracted many researchers. In this paper, a multiobjective biogeography based optimization algorithm with decomposition (MBBOD) is proposed to solve community detection problem in dynamic networks. In the proposed algorithm, the decomposition mechanism is adopted to optimize two evaluation objectives named modularity and normalized mutual information simultaneously, which measure the quality of the community partitions and temporal cost respectively. A novel sorting strategy for multiobjective biogeography based optimization is presented for comparing quality of habitats to get species counts. In addition, problem-specific migration and mutation model are introduced to improve the effectiveness of the new algorithm. Experimental results both on synthetic and real networks demonstrate that our algorithm is effective and promising, and it can detect communities more accurately in dynamic networks compared with DYNMOGA and FaceNet.

Suggested Citation

  • Zhou, Xu & Liu, Yanheng & Li, Bin & Sun, Geng, 2015. "Multiobjective biogeography based optimization algorithm with decomposition for community detection in dynamic networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 430-442.
    • Handle: RePEc:eee:phsmap:v:436:y:2015:i:c:p:430-442
    DOI: 10.1016/j.physa.2015.05.069
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    Citations

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    Cited by:

    1. 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.
    2. Liu, Qiang & Liu, Caihong & Wang, Jiajia & Wang, Xiang & Zhou, Bin & Zou, Peng, 2017. "Evolutionary link community structure discovery in dynamic weighted networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 370-388.
    3. Xin, Yu & Xie, Zhi-Qiang & Yang, Jing, 2016. "The adaptive dynamic community detection algorithm based on the non-homogeneous random walking," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 241-252.
    4. 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.
    5. Zhan, Weihua & Deng, Lei & Guan, Jihong & Niu, Jun & Sun, Dechao, 2020. "Revealing dynamic communities in networks using genetic algorithm with merge and split operators," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 558(C).

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