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Generalised popularity-similarity optimisation model for growing hyperbolic networks beyond two dimensions
Hyperbolic network models have gained considerable attention in recent years, mainly due to their capability of explaining many peculiar features of real-world networks. One of the most widely known models of this type is the popularity-similarity optimisation (PSO) model, working in the native disk...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8770586/ https://www.ncbi.nlm.nih.gov/pubmed/35046448 http://dx.doi.org/10.1038/s41598-021-04379-1 |
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author | Kovács, Bianka Balogh, Sámuel G. Palla, Gergely |
author_facet | Kovács, Bianka Balogh, Sámuel G. Palla, Gergely |
author_sort | Kovács, Bianka |
collection | PubMed |
description | Hyperbolic network models have gained considerable attention in recent years, mainly due to their capability of explaining many peculiar features of real-world networks. One of the most widely known models of this type is the popularity-similarity optimisation (PSO) model, working in the native disk representation of the two-dimensional hyperbolic space and generating networks with small-world property, scale-free degree distribution, high clustering and strong community structure at the same time. With the motivation of better understanding hyperbolic random graphs, we hereby introduce the dPSO model, a generalisation of the PSO model to any arbitrary integer dimension [Formula: see text] . The analysis of the obtained networks shows that their major structural properties can be affected by the dimension of the underlying hyperbolic space in a non-trivial way. Our extended framework is not only interesting from a theoretical point of view but can also serve as a starting point for the generalisation of already existing two-dimensional hyperbolic embedding techniques. |
format | Online Article Text |
id | pubmed-8770586 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-87705862022-01-20 Generalised popularity-similarity optimisation model for growing hyperbolic networks beyond two dimensions Kovács, Bianka Balogh, Sámuel G. Palla, Gergely Sci Rep Article Hyperbolic network models have gained considerable attention in recent years, mainly due to their capability of explaining many peculiar features of real-world networks. One of the most widely known models of this type is the popularity-similarity optimisation (PSO) model, working in the native disk representation of the two-dimensional hyperbolic space and generating networks with small-world property, scale-free degree distribution, high clustering and strong community structure at the same time. With the motivation of better understanding hyperbolic random graphs, we hereby introduce the dPSO model, a generalisation of the PSO model to any arbitrary integer dimension [Formula: see text] . The analysis of the obtained networks shows that their major structural properties can be affected by the dimension of the underlying hyperbolic space in a non-trivial way. Our extended framework is not only interesting from a theoretical point of view but can also serve as a starting point for the generalisation of already existing two-dimensional hyperbolic embedding techniques. Nature Publishing Group UK 2022-01-19 /pmc/articles/PMC8770586/ /pubmed/35046448 http://dx.doi.org/10.1038/s41598-021-04379-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Kovács, Bianka Balogh, Sámuel G. Palla, Gergely Generalised popularity-similarity optimisation model for growing hyperbolic networks beyond two dimensions |
title | Generalised popularity-similarity optimisation model for growing hyperbolic networks beyond two dimensions |
title_full | Generalised popularity-similarity optimisation model for growing hyperbolic networks beyond two dimensions |
title_fullStr | Generalised popularity-similarity optimisation model for growing hyperbolic networks beyond two dimensions |
title_full_unstemmed | Generalised popularity-similarity optimisation model for growing hyperbolic networks beyond two dimensions |
title_short | Generalised popularity-similarity optimisation model for growing hyperbolic networks beyond two dimensions |
title_sort | generalised popularity-similarity optimisation model for growing hyperbolic networks beyond two dimensions |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8770586/ https://www.ncbi.nlm.nih.gov/pubmed/35046448 http://dx.doi.org/10.1038/s41598-021-04379-1 |
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