Analysis of Networks with Missing Data with Application to the National Longitudinal Study of Adolescent Health
本文针对社会网络数据中常见的缺失数据问题，提出基于均值参数化等方法的建模框架，并通过美国青少年健康纵向研究的友谊网络数据展示其与朴素方法的定量和实质性差异。
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.