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Activity Number: 230
Type: Topic Contributed
Date/Time: Monday, August 10, 2015 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #315699
Title: Incorporating Covariates into Hierarchical Mixed Membership Stochastic Blockmodels
Author(s): Tracy Sweet*
Companies:
Keywords: Hierarchical Bayes ; Social Networks ; Mixed Membership
Abstract:

The hierarchical network modeling framework (HNM; Sweet et al 2013) extends single network social network models for use with the naturally occurring multiple networks found in the social and behavioral sciences in which multiple networks are involved. One such model is the hierarchical mixed membership stochastic blockmodel (Sweet et al 2014), which models social networks with subgroup structure. We discuss how network-level covariates can be incorporated into these models and introduce a new specification of this model to estimate covariate effects on subgroup insularity. As an empirical example, we use elementary school friendship network data to estimate the effects of teacher practices on friendship network insularity.


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