The relationship between clustering and networked Turing patterns

dc.contributor.authorLuo, Xiaofeng
dc.contributor.authorSun, Guiquan
dc.contributor.authorHe, Runzi
dc.contributor.authorAsamoah, Joshua Kiddy K.
dc.contributor.authorXue, Yakui
dc.contributor.authorChang, Lili
dc.contributor.orcid0000-0002-7066-246X
dc.date.accessioned2024-11-21T08:38:34Z
dc.date.available2024-11-21T08:38:34Z
dc.date.issued2024-07
dc.descriptionThis article is published by Chaos 2024 and is also available at 10.1063/5.0195450
dc.description.abstractNetworked Turing patterns often manifest as groups of nodes distributed on either side of the homogeneous equilibrium, exhibiting high and low density. These pattern formations are significantly influenced by network topological characteristics, such as the average degree. However, the impact of clustering on them remains inadequately understood. Here, we investigate the relationship between clustering and networked Turing patterns using classical prey–predator models. Our findings reveal that when nodes of high and low density are completely distributed on both sides of the homogeneous equilibrium, there is a linear decay in Turing patterns as global clustering coefficients increase, given a fixed node size and average degree; otherwise, this linear decay may not always hold due to the presence of high-density nodes considered as low-density nodes. This discovery provides a qualitative assessment of how clustering coefficients impact the formation of Turing patterns and may contribute to understanding why using refuges in ecosystems could enhance the stability of prey–predator systems. The results link network topological structures with the stability of prey–predator systems, offering new insights into predicting and controlling pattern formations in real-world systems from a network perspective.
dc.description.sponsorshipKNUST
dc.identifier.citationChaos 34, 073114 (2024)
dc.identifier.uri10.1063/5.0195450
dc.identifier.urihttps://ir.knust.edu.gh/handle/123456789/15991
dc.language.isoen
dc.publisherChaos
dc.titleThe relationship between clustering and networked Turing patterns
dc.typeArticle
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