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Evolutionary dynamics on sequential temporal networks

Population structure is a well-known catalyst for the evolution of cooperation and has traditionally been considered to be static in the course of evolution. Conversely, real-world populations, such as microbiome communities and online social networks, frequently show a progression from tiny, active...

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Detalles Bibliográficos
Autores principales: Sheng, Anzhi, Li, Aming, Wang, Long
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10434888/
https://www.ncbi.nlm.nih.gov/pubmed/37549167
http://dx.doi.org/10.1371/journal.pcbi.1011333
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author Sheng, Anzhi
Li, Aming
Wang, Long
author_facet Sheng, Anzhi
Li, Aming
Wang, Long
author_sort Sheng, Anzhi
collection PubMed
description Population structure is a well-known catalyst for the evolution of cooperation and has traditionally been considered to be static in the course of evolution. Conversely, real-world populations, such as microbiome communities and online social networks, frequently show a progression from tiny, active groups to huge, stable communities, which is insufficient to be captured by constant structures. Here, we propose sequential temporal networks to characterize growing networked populations, and we extend the theory of evolutionary games to these temporal networks with arbitrary structures and growth rules. We derive analytical rules under which a sequential temporal network has a higher fixation probability for cooperation than its static counterpart. Under neutral drift, the rule is simply a function of the increment of nodes and edges in each time step. But if the selection is weak, the rule is related to coalescence times on networks. In this case, we propose a mean-field approximation to calculate fixation probabilities and critical benefit-to-cost ratios with lower calculation complexity. Numerical simulations in empirical datasets also prove the cooperation-promoting effect of population growth. Our research stresses the significance of population growth in the real world and provides a high-accuracy approximation approach for analyzing the evolution in real-life systems.
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spelling pubmed-104348882023-08-18 Evolutionary dynamics on sequential temporal networks Sheng, Anzhi Li, Aming Wang, Long PLoS Comput Biol Research Article Population structure is a well-known catalyst for the evolution of cooperation and has traditionally been considered to be static in the course of evolution. Conversely, real-world populations, such as microbiome communities and online social networks, frequently show a progression from tiny, active groups to huge, stable communities, which is insufficient to be captured by constant structures. Here, we propose sequential temporal networks to characterize growing networked populations, and we extend the theory of evolutionary games to these temporal networks with arbitrary structures and growth rules. We derive analytical rules under which a sequential temporal network has a higher fixation probability for cooperation than its static counterpart. Under neutral drift, the rule is simply a function of the increment of nodes and edges in each time step. But if the selection is weak, the rule is related to coalescence times on networks. In this case, we propose a mean-field approximation to calculate fixation probabilities and critical benefit-to-cost ratios with lower calculation complexity. Numerical simulations in empirical datasets also prove the cooperation-promoting effect of population growth. Our research stresses the significance of population growth in the real world and provides a high-accuracy approximation approach for analyzing the evolution in real-life systems. Public Library of Science 2023-08-07 /pmc/articles/PMC10434888/ /pubmed/37549167 http://dx.doi.org/10.1371/journal.pcbi.1011333 Text en © 2023 Sheng et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Sheng, Anzhi
Li, Aming
Wang, Long
Evolutionary dynamics on sequential temporal networks
title Evolutionary dynamics on sequential temporal networks
title_full Evolutionary dynamics on sequential temporal networks
title_fullStr Evolutionary dynamics on sequential temporal networks
title_full_unstemmed Evolutionary dynamics on sequential temporal networks
title_short Evolutionary dynamics on sequential temporal networks
title_sort evolutionary dynamics on sequential temporal networks
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10434888/
https://www.ncbi.nlm.nih.gov/pubmed/37549167
http://dx.doi.org/10.1371/journal.pcbi.1011333
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