Bayesian Methods for Affiliation Network Analysis

• Activity Number: 33
• Type: Contributed
• Date/Time: Sunday, August 3, 2014 : 2:00 PM to 3:50 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Yanan Jia*+ and Kate Calder
• Companies: and Ohio State University
• Keywords: Bayesian modeling ; generalized linear model ; social networks ; MCMC

Abstract

An affiliation network is a particular 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 shared affiliations are known to be fundamental in defining the social identity of individuals, statistical methods for studying affiliation networks are less well developed than are 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 this approach may lead to the loss of important structural features of the data. The most comprehensive approach is to study both actors and events simultaneously. In this paper, we extend the bilinear mixed-effects model developed for one-mode networks to affiliation networks by considering dependence patterns in the interactions between actors and events. We describe a Markov chain Monte Carlo algorithm for Bayesian inference, and illustrate our methodology by examining patterns of student participation in extra-curricular activities.