Statistical Analysis of Longitudinal Network Data with Changing Composition

Markov chains can be used for the modeling of complex longitudinal network data. One class of probability models to model the evolution of social networks are stochastic actor-oriented models for network change proposed by Snijders (1996, 2001). These models are continuous-time Markov chain models that are implemented as simulation models. In this paper an extension of the simulation algorithm of stochastic actor-oriented models is proposed to include networks of changing composition. In empirical research, the composition of networks may change due to actors joining or leaving the network at some points in time. The composition changes are modeled as exogenous events that occur at given time points and are implemented in the simulation algorithm. The estimation of the network effects and the effects of actor and dyadic attributes that influence the evolution of the network, is based on the simulation of Markov chains.