Relational event (social network) history data are becoming increasingly available due to technological developments (e.g., email, sociometric badges). These data contain precise information about who was interacting with whom and at what point in time. This new type of data has the potential to greatly contribute to our understanding of dynamic social networks by providing new insights about speed, rhythm, duration, and lag in social interactions. However a crucial problem is that statistical tools for analyzing such data are currently underdeveloped.
The goal of this project is to develop novel (Bayesian) statistical methods for analyzing these time-stamped relational data. Methods need to be developed for fitting dynamic relational event models, and for testing the fit of these models. Moreover, efficient algorithms must be developed that allows users to apply these models to real-life data with ease. Finally, the methods will be used to answer substantive research questions on dynamic social networks. The project will be supervised by Dr. Ir. Joris Mulder (Dept. of Methodology & Statistics) and Prof. dr. Roger Leenders (Dept. of Organization Studies).
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