IDEAS home Printed from https://ideas.repec.org/a/taf/lstaxx/v46y2017i22p11272-11288.html
   My bibliography  Save this article

A statistical approach to social network monitoring

Author

Listed:
  • Ebrahim Mazrae Farahani
  • Reza Baradaran Kazemzadeh
  • Rassoul Noorossana
  • Ghazaleh Rahimian

Abstract

Social network monitoring consists of monitoring changes in networks with the aim of detecting significant ones and attempting to identify assignable cause(s) contributing to the occurrence of a change. This paper proposes a method that helps to overcome some of the weaknesses of the existing methods. A Poisson regression model for the probability of the number of communications between network members as a function of vertex attributes is constructed. Multivariate exponentially weighted moving average (MEWMA) and multivariate cumulative sum (MCUSUM) control charts are used to monitor the network formation process. The results indicate more efficient performance for the MEWMA chart in identifying significant changes.

Suggested Citation

  • Ebrahim Mazrae Farahani & Reza Baradaran Kazemzadeh & Rassoul Noorossana & Ghazaleh Rahimian, 2017. "A statistical approach to social network monitoring," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(22), pages 11272-11288, November.
    • Handle: RePEc:taf:lstaxx:v:46:y:2017:i:22:p:11272-11288
    DOI: 10.1080/03610926.2016.1263741
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03610926.2016.1263741
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/03610926.2016.1263741?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. Anna Malinovskaya & Philipp Otto, 2021. "Online network monitoring," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(5), pages 1337-1364, December.

    More about this item

    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:taf:lstaxx:v:46:y:2017:i:22:p:11272-11288. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/lsta .

    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.