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Community-Aware Content Diffusion: Embeddednes and Permeability

  • Letizia MilliEmail author
  • Giulio Rossetti
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 881)

Abstract

Viruses, opinions, ideas are different contents sharing a common trait: they need carriers embedded into a social context to spread. Modeling and approximating diffusive phenomena have always played an essential role in a varied range of applications from outbreak prevention to the analysis of meme and fake news. Classical approaches to such a task assume diffusion processes unfolding in a mean-field context, every actor being able to interact with all its peers. However, during the last decade, such an assumption has been progressively superseded by the availability of data modeling the real social network of individuals, thus producing a more reliable proxy for social interactions as spreading vehicles. In this work, following such a trend, we propose alternative ways of leveraging apriori knowledge on mesoscale network topology to design community-aware diffusion models with the aim of better approximate the spreading of content over complex and clustered social tissues.

Keywords

Diffusion Epidemics Community discovery 

Notes

Acknowledgements

This work is supported by the European Community’s H2020 Program under the scheme “INFRAIA-1-2014-2015: Research Infrastructures”, grant agreement #654024 “SoBigData: Social Mining & Big Data Ecosystem” (SoBigData: http://www.sobigdata.eu).

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.KDD Laboratory, ISTI-CNRPisaItaly
  2. 2.University of PisaPisaItaly

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