Detecting the ultra low dimensionality of real networks
| Autor: | M. Ángeles Serrano, Marian Boguna, Pedro Almagro Blanco |
|---|---|
| Přispěvatelé: | Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial, Universidad de Sevilla. TIC-137: Lógica, Computación e Ingeniería del Conocimiento, Agencia Estatal de Investigación. España, Generalitat de Catalunya |
| Rok vydání: | 2021 |
| Předmět: |
Social and Information Networks (cs.SI)
FOS: Computer and information sciences Physics - Physics and Society Multidisciplinary Connectome General Physics and Astronomy FOS: Physical sciences Brain Computer Science - Social and Information Networks General Chemistry Physics and Society (physics.soc-ph) General Biochemistry Genetics and Molecular Biology |
| Zdroj: | Nature communications. 13(1) |
| ISSN: | 2041-1723 |
| Popis: | 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. Agencia Estatal de Investigación PID2019-106290GB-C22/AEI/10.13039/501100011033 Generalitat de Catalunya 2017SGR1064 |
| Databáze: | OpenAIRE |
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