"Accuracy of Mean-Field Theory for Dynamics on Real-World Networks"

One of my papers was published in final form today.

Title: Accuracy of Mean-Field Theory for Dynamics on Real-World Networks

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

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.


P.S. And as a preview, watch for something special starting on Thursday at 7pm. :)