The course is designed primarily for researchers who are currently doing longitudinal social network research or who are embarking upon it. More specifically, the course is about how to analyze panel data (observed at two or more discrete moments in time) on complete social networks (all the network ties within a set of actors are observed as present or absent, except a moderate amount of missing data). The course will treat the statistical modeling of the dynamics of social networks and of the co-evolution of networks and actors, according to the actor-based approach of SIENA models. Some attention will be given also to non-longitudinal network models, the so-called Exponential Random Graph Models (ERGM).

The course will cover both theoretical and practical issues and includes extensive laboratory practice with RSIENA, the implementation of SIENA models in the R environment for statistical computing. For extensive information, materials and references about the statistical analysis of social network dynamics and the SIENA approach see the SIENA website (http://www.stats.ox.ac.uk/~snijders/siena/ )

http://www.scienzeaziendali.unibo.it/it/eventi/longitudinal-network-analysis-with-rsiena