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

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posted on 2022-12-14, 10:53 authored by James GleesonJames Gleeson, Sergey Melnik, Jonathan A Ward, Mason A Porter, Peter J Murcha
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.

Funding

Mechanical Forces and Bladder Membrane Trafficking

National Institute of Biomedical Imaging and Bioengineering

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History

Publication

Physical Review E;85, 026106

Publisher

American Physical Society

Note

peer-reviewed

Other Funding information

SFI, INSPIRE

Language

English

Also affiliated with

  • MACSI - Mathematics Application Consortium for Science & Industry

Department or School

  • Mathematics & Statistics

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