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A separable model for dynamic networks

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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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Keywords: Exponential random‐graph model; Longitudinal network; Markov chain Monte Carlo methods; Maximum likelihood estimation; Social networks

Document Type: Research Article

Publication date: 2014-01-01

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