Over the past decade, network research has increased dramatically. Network data are used in many fields because they contain not only covariates of each observation, but also `relationships' between observations. Therefore, statistical analysis of network data has been rapidly developed. However, network data presents many challenges, such as collecting network data, inferring the prevalence of an outcome of interest, and valid statistical testing typically with highly dependent data. The methods discussed in this thesis are developed to improve statistical inference from dependent network data.