Accuracy of mean-field theory for dynamics on real-world networks

James P. Gleeson, Sergey Melnik, Jonathan A. Ward, Mason A. Porter, Peter J. Mucha

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Article number026106
JournalPhysical Review E - Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics
Volume85
Issue number2
DOIs
Publication statusPublished - 7 Feb 2012

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