Unified mean-field framework for susceptible-infected-susceptible epidemics on networks, based on graph partitioning and the isoperimetric inequality

K. Devriendt, P. Van Mieghem

Research output: Contribution to journalArticleScientificpeer-review

13 Citations (Scopus)
33 Downloads (Pure)

Abstract

We propose an approximation framework that unifies and generalizes a number of existing mean-field approximation methods for the susceptible-infected-susceptible (SIS) epidemic model on complex networks. We derive the framework, which we call the unified mean-field framework (UMFF), as a set of approximations of the exact Markovian SIS equations. Our main novelty is that we describe the mean-field approximations from the perspective of the isoperimetric problem, which results in bounds on the UMFF approximation error. These new bounds provide insight in the accuracy of existing mean-field methods, such as the N-intertwined mean-field approximation and heterogeneous mean-field method, which are contained by UMFF. Additionally, the isoperimetric inequality relates the UMFF approximation accuracy to the regularity notions of Szemerédi's regularity lemma.

Original languageEnglish
Article number052314
Pages (from-to)1-18
Number of pages18
JournalPhysical Review E
Volume96
Issue number5
DOIs
Publication statusPublished - 2017

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