Community-aware content diffusion: embeddednes and permeability

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 However, during the last decade, such an assumption has been progressively superseded by the availability of data modeling the real social network of individuals, thus producing a more reliable proxy for social interactions as spreading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.