Phase I monitoring of social network with baseline periods using poisson regression

Ebrahim Mazrae Farahani and Reza Baradaran Kazemzadeh

Communications in Statistics - Theory and Methods, 2019, vol. 48, issue 2, 311-331

Abstract: Social network analysis is an important analytic tool to forecast social trends by modeling and monitoring the interactions between network members. This paper proposes an extension of a statistical process control method to monitor social networks by determining the baseline periods when the reference network set is collected. We consider probability density profile (PDP) to identify baseline periods using Poisson regression to model the communications between members. Also, Hotelling T2 and likelihood ratio test (LRT) statistics are developed to monitor the network in Phase I. The results based on signal probability indicate a satisfactory performance for the proposed method.

Date: 2019
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DOI: 10.1080/03610926.2017.1408836

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