Abstract Details
Activity Number:
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608
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Type:
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Contributed
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Date/Time:
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Wednesday, August 7, 2013 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract - #308302 |
Title:
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Bilinear Mixed Effects Models for Affiliation Networks
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Author(s):
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Yanan Jia*+ and Catherine A. Calder
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Companies:
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Ohio State University and Ohio State University
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Keywords:
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Bayesian modeling ;
generalized linear model ;
social networks ;
MCMC
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Abstract:
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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. Multiple group affiliations are fundamental in defining the social identity of individuals. But 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 the affiliation network, which is known as the conversion method. However, since affiliation networks are defined on subsets of actors and events, the conversion method loses 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 models developed for one-mode networks to affiliation networks by considering third-order dependence patterns in the interactions between actors and events. We describe a Markov chain Monte Carlo algorithm for Bayesian inference.
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Authors who are presenting talks have a * after their name.
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