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Activity Number: 410
Type: Topic Contributed
Date/Time: Tuesday, August 11, 2015 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Computing
Abstract #316182 View Presentation
Title: Bayesian Inference for Social Networks Using Aggregated Relational Data
Author(s): Shirin Golchi* and Tian Zheng
Companies: Columbia University and Columbia University
Keywords: aggregated relational data ; fully Bayesian ; hierarchical model ; scalable computation ; social network
Abstract:

Bayesian analysis has gained popularity since it facilitates use of hierarchical models in complex data settings such as analysis of network data. Inferring networks is central to many areas of science including sociology, biology and scientific literature. However, full network data is often costly to collect and analyse. An alternative to full network data is Aggregated Relational Data (ARD). In the context of social sciences, ARD are collected by adding questions of the type, "how many X's do you know?" to standard surveys where "X" is a population of interest. We propose a fully Bayesian hierarchical model and address efficient computation for network inference using ARD.


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