A Separable Model for Dynamic Networks.
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.
Pennsylvania State University, University Park, USA.
Pavel N
PN
Krivitsky
University of California at Los Angeles, Los Angeles, USA.
Mark S
MS
Handcock
Exponential random graph model; Longitudinal; Markov chain Monte Carlo; Maximum likelihood estimation; Social networks