Document - Diffusive Phenomena in Dynamic Networks: a data-driven study

2018

Conference article Open Access

Diffusive Phenomena in Dynamic Networks: a data-driven study

Milli L., Rossetti G., Pedreschi D., Giannotti F.

Diffusion Processe Information Spreading Dynamic Networks

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 been 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.

Source: 9th Conference on Complex Networks, CompleNet, pp. 151–159, Boston, USA, 6/3/2018

Metrics

Back to previous page

Cite as

BibTeX entry

@inproceedings{oai:it.cnr:prodotti:384752,
	title = {Diffusive Phenomena in Dynamic Networks: a data-driven study},
	author = {Milli L. and Rossetti G. and Pedreschi D. and Giannotti F.},
	doi = {10.1007/978-3-319-73198-8_13},
	booktitle = {9th Conference on Complex Networks, CompleNet, pp. 151–159, Boston, USA, 6/3/2018},
	year = {2018}
}