Phase I monitoring of social networks based on Poisson regression profiles

Abstract

AbstractNowadays, due to the increasing role of social networks in our daily life, monitoring and forecasting social trends have attracted the attention of many researchers. To the best of the authors' knowledge, the literature includes few studies of monitoring social networks. Existing researches have focused on analyzing only the existence of communications between people and have neglected to monitor the number of such communications. In this paper, first counts of communications between people are modeled using Poisson regression profiles. Then, 3 Phase I monitoring methods, extended T2, F, and a standardized likelihood ratio test method is suggested to detect step changes, drift, and outliers in the parameters of Poisson regression profiles. The proposed methods are evaluated via simulation studies in terms of signal probability criterion. The results show that in most out‐of‐control situations the standardized likelihood ratio test method outperforms the T2 and F methods. Then, a numerical example and a case study based on Enron email data are presented to illustrate the application of the extended methods.