@conference {1021,
	title = {Diffusive Phenomena in Dynamic Networks: a data-driven study},
	booktitle = {International Conference on Complex Networks CompleNet},
	year = {2018},
	publisher = {Springer},
	organization = {Springer},
	address = {Boston March 5-8 2018},
	abstract = {Everyday, 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 {\textendash} following a data-driven approach {\textendash} 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.},
	doi = {10.1007/978-3-319-73198-8_13},
	url = {https://link.springer.com/chapter/10.1007/978-3-319-73198-8_13},
	author = {Letizia Milli and Giulio Rossetti and Dino Pedreschi and Fosca Giannotti}
}
