A separable model for dynamic networks
- Author(s): Krivitsky, PN
- Handcock, MS
- et al.
Published Web Locationhttps://doi.org/10.1111/rssb.12014
Models of dynamic networks-networks that evolve over time-have manifold applications. We develop a discrete time generative model for social network evolution that inherits the richness and flexibility of the class of exponential family random-graph models. The model-a separable temporal exponential family random-graph model-facilitates separable modelling of the tie duration distributions and the structural dynamics of tie formation. We develop likelihood-based inference for the model and provide computational algorithms for maximum likelihood estimation. We illustrate the interpretability of the model in analysing a longitudinal network of friendship ties within a school. © 2013 Royal Statistical Society.
Many UC-authored scholarly publications are freely available on this site because of the UC Academic Senate's Open Access Policy. Let us know how this access is important for you.