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Diffusive Phenomena in Dynamic Networks: A Data-Driven Study

  • Letizia Milli
  • Giulio Rossetti
  • Dino Pedreschi
  • Fosca Giannotti
Conference paper
Part of the Springer Proceedings in Complexity book series (SPCOM)

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.

Keywords

Diffusion processes Information spreading Dynamic networks 

Notes

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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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Letizia Milli
    • 1
    • 2
  • Giulio Rossetti
    • 2
  • Dino Pedreschi
    • 1
  • Fosca Giannotti
    • 2
  1. 1.University of Pisa2 PisaItaly
  2. 2.KDD Lab. ISTI-CNR1 PisaItaly

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