A separable model for dynamic networks
type="main" xml:id="rssb12014-abs-0001"> <title type="main">Summary</title> <p>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.
Year of publication: |
2014
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Authors: | Krivitsky, Pavel N. ; Handcock, Mark S. |
Published in: |
Journal of the Royal Statistical Society Series B. - Royal Statistical Society - RSS, ISSN 1369-7412. - Vol. 76.2014, 1, p. 29-46
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Publisher: |
Royal Statistical Society - RSS |
Saved in:
Online Resource
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