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Representing Degree Distributions, Clustering, and Homophily in Social Networks With Latent Cluster Random Effects Models

Social network data often involve transitivity, homophily on observed attributes, clustering, and heterogeneity of actor degrees. We propose a latent cluster random effects model to represent all of these features, and we describe a Bayesian estimation method for it. The model is applicable to both...

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Библиографические подробности
Главные авторы: Krivitsky, Pavel N., Handcock, Mark S., Raftery, Adrian E., Hoff, Peter D.
Формат: Artigo
Язык:Inglês
Опубликовано: 2009
Предметы:
Online-ссылка:https://ncbi.nlm.nih.gov/pmc/articles/PMC2827882/
https://ncbi.nlm.nih.gov/pubmed/20191087
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