IDEAS home Printed from https://ideas.repec.org/a/zna/indecs/v14y2016i3p296-302.html
   My bibliography  Save this article

Trust model for social network using singular value decomposition

Author

Listed:
  • Davis Bundi Ntwiga

    (School of Mathematics, University of Nairobi, Nairobi, Kenya)

  • Patrick Weke

    (School of Mathematics, University of Nairobi, Nairobi, Kenya)

  • Michael Kiura Kirumbu

    (School of Sciences, Engineering and Health, Daystar University, Nairobi, Kenya)

Abstract

For effective interactions to take place in a social network, trust is important. We model trust of agents using the peer to peer reputation ratings in the network that forms a real valued matrix. Singular value decomposition discounts the reputation ratings to estimate the trust levels as trust is the subjective probability of future expectations based on current reputation ratings. Reputation and trust are closely related and singular value decomposition can estimate trust using the real valued matrix of the reputation ratings of the agents in the network. Singular value decomposition is an ideal technique in error elimination when estimating trust from reputation ratings. Reputation estimation of trust is optimal at the discounting of 20 %.

Suggested Citation

  • Davis Bundi Ntwiga & Patrick Weke & Michael Kiura Kirumbu, 2016. "Trust model for social network using singular value decomposition," Interdisciplinary Description of Complex Systems - scientific journal, Croatian Interdisciplinary Society Provider Homepage: http://indecs.eu, vol. 14(3), pages 296-302.
  • Handle: RePEc:zna:indecs:v:14:y:2016:i:3:p:296-302
    as

    Download full text from publisher

    File URL: http://indecs.eu/2016/indecs2016-pp296-302.pdf
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Ntwiga, Davis Bundi, 2018. "Credit risk analysis for low income earners," KBA Centre for Research on Financial Markets and Policy Working Paper Series 24, Kenya Bankers Association (KBA).
    2. Harish Kamath & Noor Firdoos Jahan, 2020. "Using Hidden Markov Model to Monitor Possible Loan Defaults in Banks," International Journal of Economics & Business Administration (IJEBA), International Journal of Economics & Business Administration (IJEBA), vol. 0(4), pages 1097-1107.

    More about this item

    Keywords

    singular value decomposition; reputation; trust; social network; discounting;
    All these keywords.

    JEL classification:

    • C65 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Miscellaneous Mathematical Tools

    Statistics

    Access and download statistics

    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:zna:indecs:v:14:y:2016:i:3:p:296-302. 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: Josip Stepanic (email available below). General contact details of provider: .

    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.