Work

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

Year: 2012

Type: article

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.

Source: Physical Review E

DOI: 10.1103/physreve.85.026106

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

Institutions: University of Limerick, University of Oxford, University of Reading, University of North Carolina at Chapel Hill

Language: English

Topic: Complex Network Analysis Techniques

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: green