IDEAS home Printed from https://ideas.repec.org/a/ids/ijitma/v14y2015i2-3p172-184.html
   My bibliography  Save this article

Mining top-k influential nodes in social networks via community detection

Author

Listed:
  • Wei Li
  • Jianbin Huang
  • Shuzhen Wang

Abstract

Influence maximisation is a challenging problem with high computational complexity. It aims to find a small set of seed nodes in a social network that maximises the spread of influence under a certain influence model. In this paper, we propose a community-based greedy algorithm for mining top-k influential nodes in a social network. Our method consists of two separate steps: community detection and top-k nodes mining. In the first step, we use an efficient algorithm to discover the community structure in a network. Then a 'divide and conquer' process is adopted to find the top-k influential nodes from the network. Experimental results on real-world networks show that our method is effective for mining highly influential nodes in networks. Moreover, it is more efficient than the traditional algorithms using greedy policy.

Suggested Citation

  • Wei Li & Jianbin Huang & Shuzhen Wang, 2015. "Mining top-k influential nodes in social networks via community detection," International Journal of Information Technology and Management, Inderscience Enterprises Ltd, vol. 14(2/3), pages 172-184.
  • Handle: RePEc:ids:ijitma:v:14:y:2015:i:2/3:p:172-184
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=68460
    Download Restriction: Access to full text is restricted to subscribers.

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

    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:ids:ijitma:v:14:y:2015:i:2/3:p:172-184. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Carmel O'Grady). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=18 .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.