IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v391y2012i4p1788-1796.html
   My bibliography  Save this article

Efficient algorithm based on neighborhood overlap for community identification in complex networks

Author

Listed:
  • Li, Kun
  • Gong, Xiaofeng
  • Guan, Shuguang
  • Lai, C.-H.

Abstract

Community structure is an important feature in many real-world networks. Many methods and algorithms for identifying communities have been proposed and have attracted great attention in recent years. In this paper, we present a new approach for discovering the community structure in networks. The novelty is that the algorithm uses the strength of the ties for sorting out nodes into communities. More specifically, we use the principle of weak ties hypothesis to determine to what community the node belongs. The advantages of this method are its simplicity, accuracy, and low computational cost. We demonstrate the effectiveness and efficiency of our algorithm both on real-world networks and on benchmark graphs. We also show that the distribution of link strength can give a general view of the basic structure information of graphs.

Suggested Citation

  • Li, Kun & Gong, Xiaofeng & Guan, Shuguang & Lai, C.-H., 2012. "Efficient algorithm based on neighborhood overlap for community identification in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1788-1796.
    • Handle: RePEc:eee:phsmap:v:391:y:2012:i:4:p:1788-1796
    DOI: 10.1016/j.physa.2011.09.027
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437111007655
    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.2011.09.027?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. A. Tabrizi, Shayan & Shakery, Azadeh & Asadpour, Masoud & Abbasi, Maziar & Tavallaie, Mohammad Ali, 2013. "Personalized PageRank Clustering: A graph clustering algorithm based on random walks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(22), pages 5772-5785.
    2. Zhenping Li & Xiang-Sun Zhang & Rui-Sheng Wang & Hongwei Liu & Shihua Zhang, 2013. "Discovering Link Communities in Complex Networks by an Integer Programming Model and a Genetic Algorithm," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-10, December.

    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:391:y:2012:i:4:p:1788-1796. 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.