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J R Stat Soc Series B Stat Methodol. 2014 Jan 1;76(1):29-46.

A Separable Model for Dynamic Networks.

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Pennsylvania State University, University Park, USA.
University of California at Los Angeles, Los Angeles, USA.


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 ERGM (STERGM) - facilitates separable modeling 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 analyzing a longitudinal network of friendship ties within a school.


Exponential random graph model; Longitudinal; Markov chain Monte Carlo; Maximum likelihood estimation; Social networks

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