title: Emergence of coexisting percolating clusters in networks.
abstract: It is commonly assumed in percolation theories that at most one percolating cluster can exist in a network. We show that several coexisting percolating clusters (CPCs) can emerge in networks due to limited mixing, i.e., a finite and sufficiently small number of interlinks between network modules. We develop an approach called modular message passing (MMP) to describe and verify these observations. We demonstrate that the appearance of CPCs is an important source of inaccuracy in previously introduced percolation theories, such as the message passing (MP) approach, which is a state-of-the-art theory based on the belief propagation method. Moreover, we show that the MMP theory improves significantly over the predictions of MP for percolation on synthetic networks with limited mixing and also on several real-world networks. These findings have important implications for understanding the robustness of networks and in quantifying epidemic outbreaks in the susceptible-infected-recovered (SIR) model of disease spread.
authors.affiliation: MACSI, Department of Mathematics & Statistics, University of Limerick, Limerick, Ireland.
authors.forename: Ali
authors.initials: A
authors.lastname: Faqeeh
authors.affiliation: MACSI, Department of Mathematics & Statistics, University of Limerick, Limerick, Ireland.
authors.forename: Sergey
authors.initials: S
authors.lastname: Melnik
authors.affiliation: Departament de Física Fonamental, Universitat de Barcelona, Martí i Franquès 1, 08028 Barcelona, Spain.
authors.forename: Pol
authors.initials: P
authors.lastname: Colomer-de-Simón
authors.affiliation: MACSI, Department of Mathematics & Statistics, University of Limerick, Limerick, Ireland.
authors.forename: James P
authors.initials: JP
authors.lastname: Gleeson
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D058872:Epidemics; D008954:Models, Biological; D015233:Models, Statistical; D016571:Neural Networks, Computer