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Title: A Separable Model for Dynamic Networks
Summary

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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NSF-PAR ID:
10401326
Author(s) / Creator(s):
;
Publisher / Repository:
Oxford University Press
Date Published:
Journal Name:
Journal of the Royal Statistical Society Series B: Statistical Methodology
Volume:
76
Issue:
1
ISSN:
1369-7412
Page Range / eLocation ID:
p. 29-46
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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