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Activity Number: 302 - Advances in Bayesian Computation
Type: Contributed
Date/Time: Tuesday, July 30, 2019 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract #305358 Presentation
Title: Latent Community Adaptive Network Regression
Author(s): Heather Mathews* and Alexander Volfovsky
Companies: Duke University and Duke University
Keywords: Networks; Bayesian Methods; Community detection

The study of network data in the social and health sciences frequently concentrates on detecting community structures among nodes and associating covariate information to edge formation. In much of this data, it is further likely that the effects of covariates on edge formation differ between communities (e.g. age might play a different role in friendship formation in communities across a city). In this work, we introduce an extension of the additive and multiplicative effects latent space network model where coefficients associated with certain covariates can depend on the latent community membership of the nodes. We show that ignoring such structure can lead to either over- or under-estimation of covariate importance to edge formation and propose a Markov Chain Monte Carlo approach for simultaneously learning the latent community structure and the community specific coefficients. We leverage efficient spectral methods to improve the computational tractability of our approach.

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

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