IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v421y2015icp233-248.html
   My bibliography  Save this article

Overlapping community identification approach in online social networks

Author

Listed:
  • Zhang, Xuewu
  • You, Huangbin
  • Zhu, William
  • Qiao, Shaojie
  • Li, Jianwu
  • Gutierrez, Louis Alberto
  • Zhang, Zhuo
  • Fan, Xinnan

Abstract

Online social networks have become embedded in our everyday lives so much that we cannot ignore it. One specific area of increased interest in social networks is that of detecting overlapping communities: instead of considering online communities as autonomous islands acting independently, communities are more like sprawling cities bleeding into each other. The assumption that online communities behave more like complex networks creates new challenges, specifically in the area of size and complexity. Algorithms for detecting these overlapping communities need to be fast and accurate. This research proposes method for detecting non-overlapping communities by using a CNM algorithm, which in turn allows us to extrapolate the overlapping networks. In addition, an improved index for closeness centrality is given to classify overlapping nodes. The methods used in this research demonstrate a high classification accuracy in detecting overlapping communities, with a time complexity of O(n2).

Suggested Citation

  • Zhang, Xuewu & You, Huangbin & Zhu, William & Qiao, Shaojie & Li, Jianwu & Gutierrez, Louis Alberto & Zhang, Zhuo & Fan, Xinnan, 2015. "Overlapping community identification approach in online social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 421(C), pages 233-248.
    • Handle: RePEc:eee:phsmap:v:421:y:2015:i:c:p:233-248
    DOI: 10.1016/j.physa.2014.10.095
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437114009510
    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.2014.10.095?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. Guo, Lin & Zuo, Wanli & Peng, Tao & Adhikari, Binod Kumar, 2015. "Attribute-based edge bundling for visualizing social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 48-55.

    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:421:y:2015:i:c:p:233-248. 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.