IDEAS home Printed from https://ideas.repec.org/a/ids/ijitma/v18y2019i2-3p227-242.html
   My bibliography  Save this article

SGP: a social network sampling method based on graph partition

Author

Listed:
  • Xiaolin Du
  • Dan Wang
  • Yunming Ye
  • Yan Li
  • Yueping Li

Abstract

A representative sample of a social network is essential for many internet services that rely on accurate analysis. A good sampling method for social network should be able to generate small sample network with similar structures and distributions as its original network. In this paper, a sampling algorithm based on graph partition, sampling based on graph partition (SGP), is proposed to sample social networks. SGP firstly partitions the original network into several sub-networks, and then samples in each sub-network evenly. This procedure enables SGP to effectively maintain the topological similarity and community structure similarity between the sampled network and its original network. Finally, we evaluate SGP on several well-known datasets. The experimental results show that SGP method outperforms seven state-of-the-art methods.

Suggested Citation

  • Xiaolin Du & Dan Wang & Yunming Ye & Yan Li & Yueping Li, 2019. "SGP: a social network sampling method based on graph partition," International Journal of Information Technology and Management, Inderscience Enterprises Ltd, vol. 18(2/3), pages 227-242.
    • Handle: RePEc:ids:ijitma:v:18:y:2019:i:2/3:p:227-242
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=99809
    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:18:y:2019:i:2/3:p:227-242. 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=18 .

    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.