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arXiv · 1011.1937

A Separable Model for Dynamic Networks

Pavel N. Krivitsky·Mark S. Handcock
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Abstract

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.

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BibTeXRIS

Pavel N. Krivitsky, Mark S. Handcock. 2010-11-08. A Separable Model for Dynamic Networks. https://doi.org/10.1111/rssb.12014

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