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Activity Number: 66
Type: Topic Contributed
Date/Time: Sunday, July 31, 2016 : 4:00 PM to 5:50 PM
Sponsor: Biometrics Section
Abstract #319687 View Presentation
Title: Prestige in Latent Social Space
Author(s): Gary Oehlert*
Companies: University of Minnesota
Keywords: Social Network ; Prestige ; Latent variables ; Bayesian

It is well known that members of (social) networks have a tendency to link to other members with higher prestige. For example, when colleges name their peers they preferentially select peers with higher test scores, graduation rates, endowments, and so on. Numerous authors have used this phenomenon to construct prestige orderings of the nodes; some have made fortunes doing so. This paper describes an extension of the latent social space model of Hoff, Raftery, and Handcock that introduces a prestige dimension by distorting the distance among nodes in latent social space. Along one dimension, nodes look "closer" when looking up the axis than when looking down the axis. This asymmetry induces a prestige ordering on the nodes. By using the MCMC output, we can obtain inferential details not typically available with other prestige measures.

Authors who are presenting talks have a * after their name.

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