A separable model for dynamic networks

Pavel N. Krivitsky and Mark S. Handcock

Journal of the Royal Statistical Society Series B, 2014, vol. 76, issue 1, 29-46

Abstract: type="main" xml:id="rssb12014-abs-0001">

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.

Date: 2014
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