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Accuracy of mean-field theory for dynamics on real-world networks
Gleeson, James P.; Melnik, Sergey; Ward, Jonathan A; Porter, Mason A; Murcha, Peter J
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. PUBLISHED peer-reviewed
Keyword(s): complex networks; heterogeneous networks; epidemics; spread; models
Publication Date:
2012
Type: Journal article
Peer-Reviewed: Yes
Language(s): English
Institution: University of Limerick
Funder(s): Science Foundation Ireland
Citation(s): Physical Review E;85, 026106
http://dx.doi.org/10.1103/PhysRevE.85.026106
06/IN.1/I366
06/MI/005
09/SRC/EI780
Publisher(s): American Physical Society
First Indexed: 2015-05-08 05:34:25 Last Updated: 2015-12-18 05:31:27