Locally Adaptive Dynamic Networks
This work addresses the challenge of realistically modeling dynamic contact networks, such as those in primary schools, but it appears incremental as it builds on existing latent space and stochastic differential equation approaches.
The authors tackled the problem of modeling and forecasting dynamic networks of face-to-face contacts, which exhibit periods of slow and rapid changes and dynamic heterogeneity in connectivity behaviors, by developing the LADY method, resulting in an efficient MCMC algorithm for inference and forecasting.
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.