IDEAS home Printed from https://ideas.repec.org/a/hin/jnddns/4540302.html
   My bibliography  Save this article

An Improved Spectral Clustering Community Detection Algorithm Based on Probability Matrix

Author

Listed:
  • Shuxia Ren
  • Shubo Zhang
  • Tao Wu

Abstract

The similarity graphs of most spectral clustering algorithms carry lots of wrong community information. In this paper, we propose a probability matrix and a novel improved spectral clustering algorithm based on the probability matrix for community detection. First, the Markov chain is used to calculate the transition probability between nodes, and the probability matrix is constructed by the transition probability. Then, the similarity graph is constructed with the mean probability matrix. Finally, community detection is achieved by optimizing the NCut objective function. The proposed algorithm is compared with SC, WT, FG, FluidC, and SCRW on artificial networks and real networks. Experimental results show that the proposed algorithm can detect communities more accurately and has better clustering performance.

Suggested Citation

  • Shuxia Ren & Shubo Zhang & Tao Wu, 2020. "An Improved Spectral Clustering Community Detection Algorithm Based on Probability Matrix," Discrete Dynamics in Nature and Society, Hindawi, vol. 2020, pages 1-6, June.
    • Handle: RePEc:hin:jnddns:4540302
    DOI: 10.1155/2020/4540302
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/DDNS/2020/4540302.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/DDNS/2020/4540302.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2020/4540302?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
    ---><---

    More about this item

    Statistics

    Access and download statistics

    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:hin:jnddns:4540302. 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    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.