IDEAS home Printed from https://ideas.repec.org/a/spr/infosf/v15y2013i2d10.1007_s10796-012-9366-9.html
   My bibliography  Save this article

Estimation algorithm for counting periodic orbits in complex social networks

Author

Listed:
  • Ibrahim Sorkhoh

    (Kuwait University)

  • Khaled A. Mahdi

    (Kuwait University)

  • Maytham Safar

    (Kuwait University)

Abstract

Complex networks can store information in form of periodic orbits (cycles) existing in the network. This cycle-based approach although computationally intensive, it provided us with useful information about the behavior and connectivity of the network. Social networks in most works are treated like any complex network with minimal sociological features modeled. Hence the cycle distribution will suggest the true capacity of this social network to store information. Counting cycles in complex networks is an NP-hard problem. This work proposed an efficient algorithm based on statistical mechanical based Belief Propagation (BP) algorithm to compute cycles in different complex networks using a phenomenological Gaussian distribution of cycles. The enhanced BP algorithm was applied and tested on different networks and the results showed that our model accurately approximated the cycles distribution of those networks, and that the best accuracy was obtained for the random network. In addition, a clear improvement was achieved in the cycles computation time. In some cases the execution time was reduced by up to 88 % compared to the original BP algorithm.

Suggested Citation

  • Ibrahim Sorkhoh & Khaled A. Mahdi & Maytham Safar, 2013. "Estimation algorithm for counting periodic orbits in complex social networks," Information Systems Frontiers, Springer, vol. 15(2), pages 193-202, April.
    • Handle: RePEc:spr:infosf:v:15:y:2013:i:2:d:10.1007_s10796-012-9366-9
    DOI: 10.1007/s10796-012-9366-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10796-012-9366-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10796-012-9366-9?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.

    References listed on IDEAS

    as
    1. Ralitsa Angelova & Marek Lipczak & Evangelos Milios & Pawel Pralat, 2010. "Investigating the Properties of a Social Bookmarking and Tagging Network," International Journal of Data Warehousing and Mining (IJDWM), IGI Global, vol. 6(1), pages 1-19, January.
    2. Symeon Papadopoulos & Athena Vakali & Ioannis Kompatsiaris, 2010. "The Dynamics of Content Popularity in Social Media," International Journal of Data Warehousing and Mining (IJDWM), IGI Global, vol. 6(1), pages 20-37, January.
    3. Robert Wetzker & Carsten Zimmermann & Christian Bauckhage, 2010. "Detecting Trends in Social Bookmarking Systems: A del.icio.us Endeavor," International Journal of Data Warehousing and Mining (IJDWM), IGI Global, vol. 6(1), pages 38-57, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Gabriele Kotsis & Ismail Khalil, 2013. "Special issue on Semantic Information Management guest editorial," Information Systems Frontiers, Springer, vol. 15(2), pages 151-157, April.
    2. Rezvanian, Alireza & Rahmati, Mohammad & Meybodi, Mohammad Reza, 2014. "Sampling from complex networks using distributed learning automata," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 396(C), pages 224-234.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Christian Mühlroth & Michael Grottke, 2018. "A systematic literature review of mining weak signals and trends for corporate foresight," Journal of Business Economics, Springer, vol. 88(5), pages 643-687, July.

    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:spr:infosf:v:15:y:2013:i:2:d:10.1007_s10796-012-9366-9. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.