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Accuracy of mean-field theory for dynamics on real-world networks
Date
2012
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.
Supervisor
Description
peer-reviewed
Publisher
American Physical Society
Citation
Physical Review E;85, 026106
Files
Funding code
Funding Information
Science Foundation Ireland (SFI), INSPIRE
Sustainable Development Goals
External Link
Type
Article
Rights
https://creativecommons.org/licenses/by-nc-sa/1.0/