A separable model for dynamic networks
Pavel N. Krivitsky, Mark S. Handcock
Journal of the Royal Statistical Society: Series B (Statistical Methodology) · 2014 · Volume 76 · Issue 1
Abstract
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.
Keywords
Exponential random-graph model, Longitudinal network, Markov chain Monte Carlo methods, Maximum likelihood estimation, Social networks
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