A multilayered block network model to forecast large dynamic transportation graphs: An application to US air transport

As network data become increasingly available, new opportunities arise to understand dynamic and multilayer network systems in many applied disciplines. Dynamic transportation networks have been analyzed for years by means of static graph-based indicators in order to study the temporal evolution for elevant network components, and to reveal complex dependencies that would not be easily detected by a direct inspection of the data. This paper presents a state-of-the-art latent network model to forecast multilayer dynamic graphs that are increasingly common in transportation and proposes a community-based extension to reduce the computational burden. Flexible time series analysis is obtained by modeling the probability of edges between vertices through latent Gaussian processes.The models and Bayesian inference are illustrated on a sample of 10-year data from four major airlines within the US air transportation system. Results show how the estimated latent parameters from the models are related to the airline’s connectivity dynamics, and their ability to project the multilayer graph into the future for out-of-sample full network forecasts, while stochastic block modeling allows for the identification of relevant communities and keeps estimation times within reasonable limits.