UCLA

It is common in the analysis of social network data to assume a census of the networked population of interest. Often the observations are subject to partial observation due to a known sampling or unknown missing data mechanism. However, most social network analysis ignores the problem of missing data by including only actors with complete observations. In this paper we address the modeling of networks with missing data, developing previous ideas in missing data, network modeling, and network sampling. We use several methods including the mean value parameterization to show the quantitative and substantive differences between naive and principled modeling approaches. We also develop goodness-of-fit techniques to better understand model fit. The ideas are motivated by an analysis of a friendship network from the National Longitudinal Study of Adolescent Health.