IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v436y2015icp430-442.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437115004847
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2015.05.069?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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).
    3. 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.
    4. 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.
    5. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:436:y:2015:i:c:p:430-442. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.