Volume 10 (2009): Issue 1 (January 2009)

Analysis of social network data is often hampered by non-response and missing data. Recent studies show the negative effects of missing actors and ties on the structural properties of social networks. This means that the results of social network analyses can be severely biased if missing ties were ignored and only complete cases were analyzed. To overcome the problems created by missing data, several treatment methods are proposed in the literature: model-based methods within the framework of exponential random graph models, and imputation methods. In this paper we focus on the latter group of methods, and investigate the use of some simple imputation procedures to handle missing network data. The results of a simulation study show that ignoring the missing data can have large negative effects on structural properties of the network. Missing data treatment based on simple imputation procedures, however, does also have large negative effects and simple imputations can only successfully correct for non-response in a few specific situations.

This study examines global patterns of stability and change within six longitudinal samples of English-language weblogs (or “blogs”) during the 2004 U.S. Presidential election campaign. Using distance-based methods of graph comparison, we explore the evolution of the blog-blog citation networks for each sample during the period. In addition to describing the qualitative dynamics of the blog networks, we relate major campaign events (e.g., party political conventions and debates) to the observed pace of change. As we demonstrate, such events are associated with substantial differences in overall network volatility; moreover, volatility is also shown to have strong seasonal and endogenous components. Our findings suggest that external factors (both regular and episodic) may be important drivers of network dynamics.