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 the 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.
Keywords
Cite
@article{arxiv.1011.3710,
title = {Accuracy of Mean-Field Theory for Dynamics on Real-World Networks},
author = {James P. Gleeson and Sergey Melnik and Jonathan A. Ward and Mason A. Porter and Peter J. Mucha},
journal= {arXiv preprint arXiv:1011.3710},
year = {2015}
}
Comments
12 pages, 10 figures. This version (with title changed from "How Accurate is Mean-Field Theory for Dynamics on Real-World Networks?") accepted to appear in Phys. Rev. E