IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v527y2019ics0378437119307253.html
   My bibliography  Save this article

A bio-inspired optimal network division method

Author

Listed:
  • Yang, Hanchao
  • liu, Yujia
  • Wan, Qian
  • Deng, Yong

Abstract

Network models are ubiquitous in engineering like communication, transportation and society. For many algorithms, the processing time grows exponentially as the number of nodes grows. In applications like allocating storage center in a transportation network and organizing cluster architectures in an Iot network, dividing a massive network into small no-overlap subnetworks is necessary. In this paper, a network dividing method is presented inspired by the natural phenomena of bacterial growth, division and competition. This method aims to use the bionic metrics to divided a big network evenly, because in transmission and communication areas, balancing of workload is important. Two examples utilized to illustrate the efficiency of the proposed method. A small network with 31 nodes serves to present the division result visually and a server network with 1200 nodes was used to prove the efficiency and evenness of result.

Suggested Citation

  • Yang, Hanchao & liu, Yujia & Wan, Qian & Deng, Yong, 2019. "A bio-inspired optimal network division method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 527(C).
    • Handle: RePEc:eee:phsmap:v:527:y:2019:i:c:s0378437119307253
    DOI: 10.1016/j.physa.2019.121259
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437119307253
    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.2019.121259?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. Li, Hanwen & Shang, Qiuyan & Deng, Yong, 2021. "A generalized gravity model for influential spreaders identification in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    2. Wen, Tao & Deng, Yong, 2020. "The vulnerability of communities in complex networks: An entropy approach," Reliability Engineering and System Safety, Elsevier, vol. 196(C).
    3. Zhao, Jie & Wang, Yunchuan & Deng, Yong, 2020. "Identifying influential nodes in complex networks from global perspective," Chaos, Solitons & Fractals, Elsevier, vol. 133(C).

    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:527:y:2019:i:c:s0378437119307253. 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.