dc.creator | Almagro Blanco, Pedro | es |
dc.creator | Boguñá, Marián | es |
dc.creator | Serrano, M. Ángeles | es |
dc.date.accessioned | 2022-11-23T10:36:20Z | |
dc.date.available | 2022-11-23T10:36:20Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | Almagro Blanco, P., Boguñá, M. y Serrano, M.Á. (2022). Detecting the ultra low dimensionality of real networks. Nature Communications, 13 (art.nº6096), 1-10. https://doi.org/10.1038/s41467-022-33685-z. | |
dc.identifier.issn | 2041-1723 | es |
dc.identifier.uri | https://hdl.handle.net/11441/139716 | |
dc.description.abstract | Reducing dimension redundancy to find simplifying patterns in high dimensional datasets and complex networks has become a major endeavor
in many scientific fields. However, detecting the dimensionality of their latent
space is challenging but necessary to generate efficient embeddings to be used
in a multitude of downstream tasks. Here, we propose a method to infer the
dimensionality of networks without the need for any a priori spatial embed ding. Due to the ability of hyperbolic geometry to capture the complex con nectivity of real networks, we detect ultra low dimensionality far below values
reported using other approaches. We applied our method to real networks
from different domains and found unexpected regularities, including: tissue specific biomolecular networks being extremely low dimensional; brain con nectomes being close to the three dimensions of their anatomical embedding;
and social networks and the Internet requiring slightly higher dimensionality.
Beyond paving the way towards an ultra efficient dimensional reduction, our
findings help address fundamental issues that hinge on dimensionality, such as
universality in critical behavior. | es |
dc.description.sponsorship | Agencia Estatal de Investigación PID2019-106290GB-C22/AEI/10.13039/501100011033 | es |
dc.description.sponsorship | Generalitat de Catalunya 2017SGR1064 | es |
dc.format | application/pdf | es |
dc.format.extent | 10 | es |
dc.language.iso | eng | es |
dc.publisher | Nature Research | es |
dc.relation.ispartof | Nature Communications, 13 (art.nº6096), 1-10. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.title | Detecting the ultra low dimensionality of real networks | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial | es |
dc.relation.projectID | PID2019-106290GB-C22/AEI/10.13039/501100011033 | es |
dc.relation.projectID | 2017SGR1064 | es |
dc.relation.publisherversion | https://www.nature.com/articles/s41467-022-33685-z | es |
dc.identifier.doi | 10.1038/s41467-022-33685-z | es |
dc.contributor.group | Universidad de Sevilla. TIC-137: Lógica, Computación e Ingeniería del Conocimiento | es |
dc.journaltitle | Nature Communications | es |
dc.publication.volumen | 13 | es |
dc.publication.issue | art.nº6096 | es |
dc.publication.initialPage | 1 | es |
dc.publication.endPage | 10 | es |
dc.contributor.funder | Agencia Estatal de Investigación. España | es |
dc.contributor.funder | Generalitat de Catalunya | es |
dc.description.awardwinning | Premio Mensual Publicación Científica Destacada de la US. Escuela Técnica Superior de Ingeniería Informática | |