A Separable Model for Dynamic Networks
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 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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Mar 20, 2013
- Source ID
- 10.1111/rssb.12014
Entities
People
- Mark S. Handcock
- Pavel N. Krivitsky
Organizations
- Eunice Kennedy Shriver National Institute of Child Health and Human Development
- National Institute on Drug Abuse
- National Institutes of Health
- National Science Foundation
- Office of Naval Research
- Pennsylvania State University
- University of California, Los Angeles
- University of Washington