Abstract
The analysis of spatial networks’ evolution has predominantly concentrated on the formation process of links. However, the evolution of networks is similarly shaped by the dissolution of links, which has thus far received considerably less attention. The paper presents separable temporal exponential random graph models (STERGMs) as a promising method in this context, which allows for the disentangling of both processes. Moreover, the applicability of the method to two-mode network data is demonstrated. We illustrate the use of these models for the R&D collaboration network of the German biotechnology industry as well as for testing for the relevance of different forms of proximities for its evolution. The results reveal proximities varying in their relative importance for link formation and link dissolution.
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