Monitoring attributed social networks based on count data and random effects

Abstract. This paper presents a novel approach to statistical monitoring of online social networks where the edges represent the number of communications between ties at each time stamp. Since the available methods in the literature are limited to the assumption that the set of all interacting individuals is xed during the monitoring horizon and their corresponding attributes do not change over time, the proposed method of this study tackles these limitations due to the properties of the random effect concepts. The application of appropriate parameter estimation technique is involved in the Likelihood Ratio Testing (LRT) approach considering two different statistics and the longitudinal network data are monitored. The performance of the proposed method is veried using numerical examples including simulation studies as well as an illustrative example.