Ward, JA orcid.org/0000-0002-2469-7768, Tapper, A, Simon, PL et al. (1 more author) (2022) Micro-scale foundation with error quantification for the approximation of dynamics on networks. Communications Physics, 5. 71. ISSN 2399-3650
Abstract
Epidemics, voting behaviour and cascading failures in power grids are examples of natural, social and technological phenomena that can be modelled as dynamical processes on networks. The study of such important complex systems requires approximation, but the assumptions that underpin the standard mean-field approaches are routinely violated by dynamics on real-world networks, leading to uncontrolled errors and even controversial results. Consequently, determining the approximation precision has been recognised as a key challenge. We present a micro-scale foundation for mean-field approximation of a wide range of dynamics on networks that facilitates quantification of approximation error, elucidating its connection to network structure and model dynamics. We show that our coarse-graining approach minimises approximation error and we obtain an upper bound on this uncertainty. We illustrate our approach using epidemic dynamics on real-world networks.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © The Author(s) 2022. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Applied Mathematics (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 21 Feb 2022 10:52 |
Last Modified: | 13 Jan 2025 14:37 |
Published Version: | https://www.nature.com/articles/s42005-022-00834-1... |
Status: | Published |
Publisher: | Nature Research |
Identification Number: | 10.1038/s42005-022-00834-1 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:183708 |