01271nas a2200145   4500008004100000245006500041210006300106260003600169520077200205100001900977700002100996700002001017700002101037856006701058       2018                        eng d00aDiffusive Phenomena in Dynamic Networks: a data-driven study0 aDiffusive Phenomena in Dynamic Networks a datadriven study  aBoston March 5-8 2018bSpringer3 aEveryday, ideas, information as well as viruses spread over complex social tissues described by our interpersonal relations. So far, the network contexts upon which diffusive phenomena unfold have usually considered static, composed by a fixed set of nodes and edges. Recent studies describe social networks as rapidly changing topologies. In this work – following a data-driven approach – we compare the behaviors of classical spreading models when used to analyze a given social network whose topological dynamics are observed at different temporal-granularities. Our goal is to shed some light on the impacts that the adoption of a static topology has on spreading simulations as well as to provide an alternative formulation of two classical diffusion models.1 aMilli, Letizia1 aRossetti, Giulio1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://link.springer.com/chapter/10.1007/978-3-319-73198-8_13