title: Accuracy of mean-field theory for dynamics on real-world networks.
abstract: Mean-field analysis is an important tool for understanding dynamics on complex networks. However, surprisingly little attention has been paid to the question of whether mean-field predictions are accurate, and this is particularly true for real-world networks with clustering and modular structure. In this paper, we compare mean-field predictions to numerical simulation results for dynamical processes running on 21 real-world networks and demonstrate that the accuracy of such theory depends not only on the mean degree of the networks but also on the mean first-neighbor degree. We show that mean-field theory can give (unexpectedly) accurate results for certain dynamics on disassortative real-world networks even when the mean degree is as low as 4.
authors.affiliation: MACSI, Department of Mathematics & Statistics, University of Limerick, Ireland.
authors.forename: James P
authors.initials: JP
authors.lastname: Gleeson
authors.forename: Sergey
authors.initials: S
authors.lastname: Melnik
authors.forename: Jonathan A
authors.initials: JA
authors.lastname: Ward
authors.forename: Mason A
authors.initials: MA
authors.lastname: Porter
authors.forename: Peter J
authors.initials: PJ
authors.lastname: Mucha