Bilinear Mixed Effects Models for Affiliation Networks: Segregation of Adolescents in Their Extracurricular Activities

• Activity Number: 492
• Type: Contributed
• Date/Time: Wednesday, August 12, 2015 : 8:30 AM to 10:20 AM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Yanan Jia* and Catherine Calder and Christopher Browning
• Companies: The Ohio State University and The Ohio State University and The Ohio State University
• Keywords: Bayesian modeling ; generalized linear model ; social networks ; MCMC ; segregation

Abstract

An affiliation network is a special kind of two-mode social network that consists of a set of 'actors' and a set of 'events' where ties indicate an actor's participation in an event. While event affiliations are fundamental in defining the social identity of individuals, methods for studying affiliation networks are less well developed than methods for studying one-mode, or actor-actor, networks. One way to analyze affiliation networks is to consider one-mode network matrices which are derived from an affiliation network, but it may lead to the lose of important structural features of the data. The most comprehensive approach is to study both actors and events simultaneously. In this talk, we extend our bilinear mixed effects model developed for two-mode affiliation networks. We describe a Markov chain Monte Carlo algorithm for Bayesian inference and illustrate the proposed methodology through an analysis of a segregation of adolescents in their extracurricular activities.