IDEAS home Printed from https://ideas.repec.org/a/wsi/ijmpcx/v24y2013i08ns0129183113500551.html
   My bibliography  Save this article

Empirical Analysis Of The Clustering Coefficient In The User-Object Bipartite Networks

Author

Listed:
  • JIANGUO LIU

    (Research Center of Complex Systems Science, University of Shanghai for Science and Tecnology, Shanghai 200093, P. R. China)

  • LEI HOU

    (Research Center of Complex Systems Science, University of Shanghai for Science and Tecnology, Shanghai 200093, P. R. China)

  • YI-LU ZHANG

    (Research Center of Complex Systems Science, University of Shanghai for Science and Tecnology, Shanghai 200093, P. R. China)

  • WEN-JUN SONG

    (Research Center of Complex Systems Science, University of Shanghai for Science and Tecnology, Shanghai 200093, P. R. China)

  • XUE PAN

    (Research Center of Complex Systems Science, University of Shanghai for Science and Tecnology, Shanghai 200093, P. R. China)

Abstract

The clustering coefficient of the bipartite network,C4, has been widely used to investigate the statistical properties of the user-object systems. In this paper, we empirically analyze the evolution patterns ofC4for a nine year MovieLens data set, whereC4is used to describe the diversity of the user interest. First, we divide the MovieLens data set into fractions according to the time intervals and calculateC4of each fraction. The empirical results show that, the diversity of the user interest changes periodically with a round of one year, which reaches the smallest value in spring, then increases to the maximum value in autumn and begins to decrease in winter. Furthermore, a null model is proposed to compare with the empirical results, which is constructed in the following way. Each user selects each object with a turnable probabilityp, and the numbers of users and objects are equal to that of the real MovieLens data set. The comparison result indicates that the user activity has greatly influenced the structure of the user-object bipartite network, and users with the same degree information may have two totally different clustering coefficients. On the other hand, the same clustering coefficient also corresponds to different degrees. Therefore, we need to take the clustering coefficient into consideration together with the degree information when describing the user selection activity.

Suggested Citation

  • Jianguo Liu & Lei Hou & Yi-Lu Zhang & Wen-Jun Song & Xue Pan, 2013. "Empirical Analysis Of The Clustering Coefficient In The User-Object Bipartite Networks," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 24(08), pages 1-10.
    • Handle: RePEc:wsi:ijmpcx:v:24:y:2013:i:08:n:s0129183113500551
    DOI: 10.1142/S0129183113500551
    as

    Download full text from publisher

    File URL: http://www.worldscientific.com/doi/abs/10.1142/S0129183113500551
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://libkey.io/10.1142/S0129183113500551?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. Li, Sheng-Nan & Guo, Qiang & Yang, Kai & Liu, Jian-Guo & Zhang, Yi-Cheng, 2018. "Uncovering the popularity mechanisms for Facebook applications," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 494(C), pages 422-429.
    2. Leilei Wu & Zhuoming Ren & Xiao-Long Ren & Jianlin Zhang & Linyuan Lü, 2018. "Eliminating the Effect of Rating Bias on Reputation Systems," Complexity, Hindawi, vol. 2018, pages 1-11, February.
    3. Zhang, Chu-Xu & Zhang, Zi-Ke & Yu, Lu & Liu, Chuang & Liu, Hao & Yan, Xiao-Yong, 2014. "Information filtering via collaborative user clustering modeling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 396(C), pages 195-203.

    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:wsi:ijmpcx:v:24:y:2013:i:08:n:s0129183113500551. 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: Tai Tone Lim (email available below). General contact details of provider: http://www.worldscinet.com/ijmpc/ijmpc.shtml .

    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.