  The increasingly crucial role of human displacements in complex societal
phenomena, such as traffic congestion, segregation, and the diffusion of
epidemics, is attracting the interest of scientists from several disciplines.
In this article, we address mobility network generation, i.e., generating a
city's entire mobility network, a weighted directed graph in which nodes are
geographic locations and weighted edges represent people's movements between
those locations, thus describing the entire mobility set flows within a city.
Our solution is MoGAN, a model based on Generative Adversarial Networks (GANs)
to generate realistic mobility networks. We conduct extensive experiments on
public datasets of bike and taxi rides to show that MoGAN outperforms the
classical Gravity and Radiation models regarding the realism of the generated
networks. Our model can be used for data augmentation and performing
simulations and what-if analysis.
