Ontology highlight
ABSTRACT:
SUBMITTER: Krivitsky PN
PROVIDER: S-EPMC2827882 | biostudies-other | 2009 Jul
REPOSITORIES: biostudies-other
Krivitsky Pavel N PN Handcock Mark S MS Raftery Adrian E AE Hoff Peter D PD
Social networks 20090701 3
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 binary and non-binary network data. We illustrate the model using two real datasets. We also apply it to two simulated network datasets with the same, highly skewed, degree distribution, but very diff ...[more]