IDEAS home Printed from https://ideas.repec.org/a/ids/ijdmmm/v14y2022i1p63-88.html
   My bibliography  Save this article

Fast parallel computation of PageRank scores with improved convergence time

Author

Listed:
  • Hema Dubey
  • Nilay Khare

Abstract

PageRank is a conspicuous link-based approach used by many search engines in order to rank its search results. PageRank algorithm is based on performing iterations for calculating PageRank of web pages until the convergent point is met. The computational cost of this algorithm is very high for very large web graphs. So to overcome this drawback, in this paper we have proposed a fast parallel computation of PageRank which uses standard deviation technique to normalise the PageRank score of each web page. The proposed work is experimented on standard datasets taken from Stanford large network dataset collection, on a machine having multicore architecture using CUDA programming paradigm. We observed from the experiments that the proposed fast parallel PageRank algorithm needs lesser number of iterations to converge as compared to existing parallel PageRank method. We also determined that there is a speed up of about 2 to 10 for nine different standard datasets for the proposed algorithm over the existing algorithm.

Suggested Citation

  • Hema Dubey & Nilay Khare, 2022. "Fast parallel computation of PageRank scores with improved convergence time," International Journal of Data Mining, Modelling and Management, Inderscience Enterprises Ltd, vol. 14(1), pages 63-88.
    • Handle: RePEc:ids:ijdmmm:v:14:y:2022:i:1:p:63-88
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=122039
    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:ijdmmm:v:14:y:2022:i:1:p:63-88. 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=342 .

    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.