Locally adaptive dynamic networks
Daniele Durante, David B. Dunson
The Annals of Applied Statistics · 2016 · Volume 10 · Issue 4
Abstract
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.
Keywords
Face-to-face dynamic contact network, latent space, nested Gaussian process, online updating, Pólya-gamma, state-space model
Enable JavaScript for citation tools and additional paper interactions.