Open Access
December 2016 Locally adaptive dynamic networks
Daniele Durante, David B. Dunson
Ann. Appl. Stat. 10(4): 2203-2232 (December 2016). DOI: 10.1214/16-AOAS971


Our focus is on realistically modeling and forecasting dynamic networks of face-to-face contacts among individuals. Important aspects of such data that lead to problems with current methods include the tendency of the contacts to move between periods of slow and rapid changes, and the dynamic heterogeneity in the actors’ connectivity behaviors. Motivated by this application, we develop a novel method for Locally Adaptive DYnamic (LADY) network inference. The proposed model relies on a dynamic latent space representation in which each actor’s position evolves in time via stochastic differential equations. Using a state-space representation for these stochastic processes and Pólya-gamma data augmentation, we develop an efficient MCMC algorithm for posterior inference along with tractable procedures for online updating and forecasting of future networks. We evaluate performance in simulation studies, and consider an application to face-to-face contacts among individuals in a primary school.


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Daniele Durante. David B. Dunson. "Locally adaptive dynamic networks." Ann. Appl. Stat. 10 (4) 2203 - 2232, December 2016.


Received: 1 May 2015; Revised: 1 August 2016; Published: December 2016
First available in Project Euclid: 5 January 2017

zbMATH: 06688774
MathSciNet: MR3592054
Digital Object Identifier: 10.1214/16-AOAS971

Keywords: Face-to-face dynamic contact network , latent space , nested Gaussian process , online updating , Pólya-gamma , state-space model

Rights: Copyright © 2016 Institute of Mathematical Statistics

Vol.10 • No. 4 • December 2016
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