A separable model for dynamic networks

• 成果类型: Article
• 署名作者: Krivitsky, Pavel N.; Handcock, Mark S.
• 署名单位: Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; University of California System; University of California Los Angeles
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/rssb.12014
• 发表日期: 2014
• 页码: 29-46
• 关键词: exponential family models; discrete temporal models

摘要

Models of dynamic networksnetworks that evolve over timehave 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 modela separable temporal exponential family random-graph modelfacilitates 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.