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Activity Number: 492
Type: Contributed
Date/Time: Wednesday, August 12, 2015 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract #316333
Title: Compact Bayesian Models of Massive Social Graphs
Author(s): Zehang Li* and Tyler McCormick and Joshua Blumenstock
Companies: University of Washington and University of Washington and University of Washington
Keywords: Social networks ; Bayesian methods ; Sparse coding ; Graph isomorphism
Abstract:

A core challenge in the statistical modeling of networks involves representing graph structure in a manner that is consistent with the models and constructs used by social scientists. This challenge is exacerbated as new sources of data on human interactions and social behavior make possible the empirical analysis of dynamics networks with millions of nodes and billions of edges. In this work, we develop a parsimonious model that captures dynamic, local structure in large-scale social networks. We first develop a method for quantifying the local structure by defining ego network representation through collections of isomorphic subgraphs. We then utilize a Bayesian variant of sparse coding to provide interpretable descriptions of dependence structure in these data. Using a large dataset of mobile phone call detail records (CDR), we demonstrate how the model can be used to predict patterns of technology diffusion across the network.


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

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