A Separable Model for Dynamic Networks
By Pavel N. Krivitsky, Mark S. Handcock. Journal of The Royal Statistical Society Series B-statistical Methodology, 2010
481 citations recorded by the source corpus.
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.
Subfields: Sociology and Political Science, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/adcdebb1f4dbea84b89267f7a023699a2534ae0a
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In Sociology and Political Science
In Statistical and Nonlinear Physics
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Citations in the corpus
- Modern temporal network theory: a colloquium (2015, background): “[200,152]) allow nodes and links to disappear too.”