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A statistical approach to social network monitoring

Author

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  • 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
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    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.

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