Multilayer representation of collaboration networks with higher-order interactions

Abstract Collaboration patterns offer important insights into how scientific breakthroughs and innovations emerge in small and large research groups. However, links in traditional networks account only for pairwise interactions, thus making the framework best suited for the description of two-person...

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Autores principales:	E. Vasilyeva, A. Kozlov, K. Alfaro-Bittner, D. Musatov, A. M. Raigorodskii, M. Perc, S. Boccaletti
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/4b639ea0915148148d8e4267fca3b3f4
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spelling	oai:doaj.org-article:4b639ea0915148148d8e4267fca3b3f42021-12-02T15:54:13ZMultilayer representation of collaboration networks with higher-order interactions10.1038/s41598-021-85133-52045-2322https://doaj.org/article/4b639ea0915148148d8e4267fca3b3f42021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-85133-5https://doaj.org/toc/2045-2322Abstract Collaboration patterns offer important insights into how scientific breakthroughs and innovations emerge in small and large research groups. However, links in traditional networks account only for pairwise interactions, thus making the framework best suited for the description of two-person collaborations, but not for collaborations in larger groups. We therefore study higher-order scientific collaboration networks where a single link can connect more than two individuals, which is a natural description of collaborations entailing three or more people. We also consider different layers of these networks depending on the total number of collaborators, from one upwards. By doing so, we obtain novel microscopic insights into the representativeness of researchers within different teams and their links with others. In particular, we can follow the maturation process of the main topological features of collaboration networks, as we consider the sequence of graphs obtained by progressively merging collaborations from smaller to bigger sizes starting from the single-author ones. We also perform the same analysis by using publications instead of researchers as network nodes, obtaining qualitatively the same insights and thus confirming their robustness. We use data from the arXiv to obtain results specific to the fields of physics, mathematics, and computer science, as well as to the entire coverage of research fields in the database.E. VasilyevaA. KozlovK. Alfaro-BittnerD. MusatovA. M. RaigorodskiiM. PercS. BoccalettiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q E. Vasilyeva A. Kozlov K. Alfaro-Bittner D. Musatov A. M. Raigorodskii M. Perc S. Boccaletti Multilayer representation of collaboration networks with higher-order interactions
description	Abstract Collaboration patterns offer important insights into how scientific breakthroughs and innovations emerge in small and large research groups. However, links in traditional networks account only for pairwise interactions, thus making the framework best suited for the description of two-person collaborations, but not for collaborations in larger groups. We therefore study higher-order scientific collaboration networks where a single link can connect more than two individuals, which is a natural description of collaborations entailing three or more people. We also consider different layers of these networks depending on the total number of collaborators, from one upwards. By doing so, we obtain novel microscopic insights into the representativeness of researchers within different teams and their links with others. In particular, we can follow the maturation process of the main topological features of collaboration networks, as we consider the sequence of graphs obtained by progressively merging collaborations from smaller to bigger sizes starting from the single-author ones. We also perform the same analysis by using publications instead of researchers as network nodes, obtaining qualitatively the same insights and thus confirming their robustness. We use data from the arXiv to obtain results specific to the fields of physics, mathematics, and computer science, as well as to the entire coverage of research fields in the database.
format	article
author	E. Vasilyeva A. Kozlov K. Alfaro-Bittner D. Musatov A. M. Raigorodskii M. Perc S. Boccaletti
author_facet	E. Vasilyeva A. Kozlov K. Alfaro-Bittner D. Musatov A. M. Raigorodskii M. Perc S. Boccaletti
author_sort	E. Vasilyeva
title	Multilayer representation of collaboration networks with higher-order interactions
title_short	Multilayer representation of collaboration networks with higher-order interactions
title_full	Multilayer representation of collaboration networks with higher-order interactions
title_fullStr	Multilayer representation of collaboration networks with higher-order interactions
title_full_unstemmed	Multilayer representation of collaboration networks with higher-order interactions
title_sort	multilayer representation of collaboration networks with higher-order interactions
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/4b639ea0915148148d8e4267fca3b3f4
work_keys_str_mv	AT evasilyeva multilayerrepresentationofcollaborationnetworkswithhigherorderinteractions AT akozlov multilayerrepresentationofcollaborationnetworkswithhigherorderinteractions AT kalfarobittner multilayerrepresentationofcollaborationnetworkswithhigherorderinteractions AT dmusatov multilayerrepresentationofcollaborationnetworkswithhigherorderinteractions AT amraigorodskii multilayerrepresentationofcollaborationnetworkswithhigherorderinteractions AT mperc multilayerrepresentationofcollaborationnetworkswithhigherorderinteractions AT sboccaletti multilayerrepresentationofcollaborationnetworkswithhigherorderinteractions
_version_	1718385445885181952

Multilayer representation of collaboration networks with higher-order interactions

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