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Open Access Publications from the University of California

A separable model for dynamic networks

  • Author(s): Krivitsky, PN
  • Handcock, MS
  • et al.

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.

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