Locally Adaptive Dynamic Networks

TL;DR

This paper introduces LADY, a novel locally adaptive method for modeling and forecasting dynamic face-to-face contact networks, capturing heterogeneity and rapid changes in social interactions.

Contribution

The paper develops a new dynamic latent space model with stochastic differential equations and efficient inference algorithms for real-time network analysis.

Findings

Effective in simulation studies

Successfully applied to primary school contact data

Outperforms existing methods in capturing rapid network changes

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 P\'olya-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.