Locally adaptive dynamic networks

Journal Article (Journal Article)

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.

Full Text

Duke Authors

Cited Authors

  • Durante, D; Dunson, DB

Published Date

  • December 1, 2016

Published In

Volume / Issue

  • 10 / 4

Start / End Page

  • 2203 - 2232

Electronic International Standard Serial Number (EISSN)

  • 1941-7330

International Standard Serial Number (ISSN)

  • 1932-6157

Digital Object Identifier (DOI)

  • 10.1214/16-AOAS971

Citation Source

  • Scopus