The inherent community structure of hyperbolic networks

Abstract A remarkable approach for grasping the relevant statistical features of real networks with the help of random graphs is offered by hyperbolic models, centred around the idea of placing nodes in a low-dimensional hyperbolic space, and connecting node pairs with a probability depending on the...

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Autores principales:	Bianka Kovács, Gergely Palla
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/53e1ca081e5046d285c51ec82cdc5cb0
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spelling	oai:doaj.org-article:53e1ca081e5046d285c51ec82cdc5cb02021-12-02T14:53:35ZThe inherent community structure of hyperbolic networks10.1038/s41598-021-93921-22045-2322https://doaj.org/article/53e1ca081e5046d285c51ec82cdc5cb02021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-93921-2https://doaj.org/toc/2045-2322Abstract A remarkable approach for grasping the relevant statistical features of real networks with the help of random graphs is offered by hyperbolic models, centred around the idea of placing nodes in a low-dimensional hyperbolic space, and connecting node pairs with a probability depending on the hyperbolic distance. It is widely appreciated that these models can generate random graphs that are small-world, highly clustered and scale-free at the same time; thus, reproducing the most fundamental common features of real networks. In the present work, we focus on a less well-known property of the popularity-similarity optimisation model and the $${\mathbb {S}}^1/{\mathbb {H}}^2$$ S 1 / H 2 model from this model family, namely that the networks generated by these approaches also contain communities for a wide range of the parameters, which was certainly not an intention at the design of the models. We extracted the communities from the studied networks using well-established community finding methods such as Louvain, Infomap and label propagation. The observed high modularity values indicate that the community structure can become very pronounced under certain conditions. In addition, the modules found by the different algorithms show good consistency, implying that these are indeed relevant and apparent structural units. Since the appearance of communities is rather common in networks representing real systems as well, this feature of hyperbolic models makes them even more suitable for describing real networks than thought before.Bianka KovácsGergely PallaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-18 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Bianka Kovács Gergely Palla The inherent community structure of hyperbolic networks
description	Abstract A remarkable approach for grasping the relevant statistical features of real networks with the help of random graphs is offered by hyperbolic models, centred around the idea of placing nodes in a low-dimensional hyperbolic space, and connecting node pairs with a probability depending on the hyperbolic distance. It is widely appreciated that these models can generate random graphs that are small-world, highly clustered and scale-free at the same time; thus, reproducing the most fundamental common features of real networks. In the present work, we focus on a less well-known property of the popularity-similarity optimisation model and the $${\mathbb {S}}^1/{\mathbb {H}}^2$$ S 1 / H 2 model from this model family, namely that the networks generated by these approaches also contain communities for a wide range of the parameters, which was certainly not an intention at the design of the models. We extracted the communities from the studied networks using well-established community finding methods such as Louvain, Infomap and label propagation. The observed high modularity values indicate that the community structure can become very pronounced under certain conditions. In addition, the modules found by the different algorithms show good consistency, implying that these are indeed relevant and apparent structural units. Since the appearance of communities is rather common in networks representing real systems as well, this feature of hyperbolic models makes them even more suitable for describing real networks than thought before.
format	article
author	Bianka Kovács Gergely Palla
author_facet	Bianka Kovács Gergely Palla
author_sort	Bianka Kovács
title	The inherent community structure of hyperbolic networks
title_short	The inherent community structure of hyperbolic networks
title_full	The inherent community structure of hyperbolic networks
title_fullStr	The inherent community structure of hyperbolic networks
title_full_unstemmed	The inherent community structure of hyperbolic networks
title_sort	inherent community structure of hyperbolic networks
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/53e1ca081e5046d285c51ec82cdc5cb0
work_keys_str_mv	AT biankakovacs theinherentcommunitystructureofhyperbolicnetworks AT gergelypalla theinherentcommunitystructureofhyperbolicnetworks AT biankakovacs inherentcommunitystructureofhyperbolicnetworks AT gergelypalla inherentcommunitystructureofhyperbolicnetworks
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The inherent community structure of hyperbolic networks

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