Statistical monitoring of dynamic networks is a major topic of interest in complex social systems. Many researches have been conducted on modeling and monitoring dynamic social networks. This article proposes a new methodology for modeling and monitoring dynamic social networks for quick detection of temporal anomalies in network structures using latent variables. The key idea behind our proposed methodology is to determine the importance of latent variables in creating edges between nodes as well as observed covariates. First, latent space model (LSM) is used to model dynamic networks. Vector of parameters in LSM model are monitored through multivariate control charts in order to detect changes in different network sizes. Experiments on simulated social network monitoring demonstrate that our surveillance monitoring strategy can effectively detect abrupt changes between actors in dynamic networks using latent variables.