Epidemic spreading on complex networks with community structures

TL;DR

This paper investigates how community structures in complex networks influence epidemic spreading, revealing that mesoscopic community arrangements significantly affect diffusion processes, while internal community details are less impactful.

Contribution

It introduces two random graph models that preserve different aspects of community structure to analyze their effects on percolation processes.

Findings

Community structure can both promote and inhibit diffusion.

Mesoscopic community arrangements are crucial for diffusion behavior.

Internal community details have minimal impact on percolation processes.

Abstract

Many real-world networks display a community structure. We study two random graph models that create a network with similar community structure as a given network. One model preserves the exact community structure of the original network, while the other model only preserves the set of communities and the vertex degrees. These models show that community structure is an important determinant of the behavior of percolation processes on networks, such as information diffusion or virus spreading: the community structure can both \textit{enforce} as well as \textit{inhibit} diffusion processes. Our models further show that it is the mesoscopic set of communities that matters. The exact internal structures of communities barely influence the behavior of percolation processes across networks. This insensitivity is likely due to the relative denseness of the communities.