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 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.
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All methods were made available within the NDLib library: https://goo.gl/1tstvG.
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Acknowledgments
This work is funded by the EU’s H2020 Program under the funding scheme “FETPROACT-1-2014: Global Systems Science (GSS),” grant agreement # 641191 CIMPLEX and under the scheme “INFRAIA-1-2014-2015: Research Infrastructures,” grant agreement # 654024 “SoBigData”.
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Milli, L., Rossetti, G., Pedreschi, D., Giannotti, F. (2018). Diffusive Phenomena in Dynamic Networks: A Data-Driven Study. In: Cornelius, S., Coronges, K., Gonçalves, B., Sinatra, R., Vespignani, A. (eds) Complex Networks IX. CompleNet 2018. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-319-73198-8_13
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DOI: https://doi.org/10.1007/978-3-319-73198-8_13
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