Dynamic Analyses of Dark Networks

TL;DRAbstract

Networks are dynamic. They evolve and change over time as actors enter, leave, and move around. Capturing dynamics such as these and others can be difficult but it is possible. This chapter's purpose is to introduce readers to some of these approaches and demonstrate how to carry out relatively painless but potentially illuminating explorations of dynamic network data. We begin by examining approaches for exploring longitudinal networks both descriptively and statistically, although we do not consider in any great detail highly sophisticated approaches to the analysis of longitudinal network data, such as the actor-based models implemented by SIENA software (see, however, Murphy, Everton, and Cunningham 2012). A comprehensive exploration of these models deserves its own book and requires software other than UCINET, Pajek, or ORA. Next we turn to the fusion of social network and geospatial data, which allows analysts to not only geospatially plot social network data but also to calculat

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