Monitoring Social Networks Based On Zero-Inflated Poisson Regression Model

Methods for monitoring network in the literature mainly use two approaches. One approach assumes a statistical model generated the network data and monitors the estimated parameters of this statistical model over time. The other approach summarizes a network using centrality measurements and then monitor these values. In this article, we assume zero-inflated Poisson regression as an underlying distribution to model the interactions between social actors. We show the applicability of the model in real data set. To monitor social networks based on this model we propose a Likelihood Ratio Test which examines the hypothesis of no change against hypotheses of change overtime. An EWMA chart is also developed to monitor the average degree centrality measurement. The performance of the proposed methods is investigated using simulation studies.