Title: Prestige in Latent Social Space, with Application to Ranking in Social Networks
Abstract: A network is a set of nodes together with their directed pairwise interconnections, either binary (present or absent) or valued (counted). Prestige is the tendency for some nodes in the network to receive more connections. The latent social space model for networks posits unobserved positions for each node, with the probabilities of interconnections dependent on the internode distances in latent space. Here we define prestige in latent social space, discuss inference and ranking based on prestige, and illustrate the method with examples from American university peers and citations between journals in Statistics.
Bio: Gary W. Oehlert is a Professor in the School of Statistics at the University of Minnesota. He received his Ph.D. in Statistics from Yale University in 1981 and taught at Princeton University for three years before coming to Minnesota in 1984. His interests in applied statistics include environmental statistics, design of experiments, and statistical computing.
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