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A separable model for dynamic networks

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  • Pavel N. Krivitsky
  • Mark S. Handcock

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.

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  • Pavel N. Krivitsky & Mark S. Handcock, 2014. "A separable model for dynamic networks," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 76(1), pages 29-46, January.
    • Handle: RePEc:bla:jorssb:v:76:y:2014:i:1:p:29-46
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    File URL: http://hdl.handle.net/10.1111/rssb.2013.76.issue-1
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