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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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Autors principals: Krivitsky, Pavel N., Handcock, Mark S., Raftery, Adrian E., Hoff, Peter D.
Format: Artigo
Idioma:Inglês
Publicat: 2009
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Accés en línia:https://ncbi.nlm.nih.gov/pmc/articles/PMC2827882/
https://ncbi.nlm.nih.gov/pubmed/20191087
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