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A separable model for dynamic networks

Journal Article


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 likelihoodbased

    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.

Publication Date


  • 2014

Citation


  • Krivitsky, P. N.. & Handcock, M. (2014). A separable model for dynamic networks. Journal of the Royal Statistical Society Series B: Statistical Methodology, 76 (1), 29-46.

Scopus Eid


  • 2-s2.0-84891827232

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/2793

Has Global Citation Frequency


Number Of Pages


  • 17

Start Page


  • 29

End Page


  • 46

Volume


  • 76

Issue


  • 1

Place Of Publication


  • United Kingdom

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 likelihoodbased

    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.

Publication Date


  • 2014

Citation


  • Krivitsky, P. N.. & Handcock, M. (2014). A separable model for dynamic networks. Journal of the Royal Statistical Society Series B: Statistical Methodology, 76 (1), 29-46.

Scopus Eid


  • 2-s2.0-84891827232

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/2793

Has Global Citation Frequency


Number Of Pages


  • 17

Start Page


  • 29

End Page


  • 46

Volume


  • 76

Issue


  • 1

Place Of Publication


  • United Kingdom