01517nas a2200169   4500008004100000245007000041210006900111300000700180490000700187520095100194100002001145700002301165700001901188700001701207700002101224856010201245       2022                        eng d00aGenerating mobility networks with generative adversarial networks0 aGenerating mobility networks with generative adversarial network  a580 v113 aThe 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.1 aMauro, Giovanni1 aLuca, Massimiliano1 aLonga, Antonio1 aLepri, Bruno1 aPappalardo, Luca  uhttps://kdd.isti.cnr.it/publications/generating-mobility-networks-generative-adversarial-networks