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A separable model for dynamic networks
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DOI：10.1111/rssb.12014.png)
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摘要
En 中文
Models of dynamic networksnetworks that evolve over timehave 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 modela separable temporal exponential family random-graph modelfacilitates 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.
Keyword:
Exponential random-graph model
Longitudinal network
Markov chain Monte Carlo methods
Maximum likelihood estimation
Social networks
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