Machine learning meets complex networks via coalescent embedding in the hyperbolic space

Physicists recently observed that realistic complex networks emerge as discrete samples from a continuous hyperbolic geometry enclosed in a circle: the radius represents the node centrality and the angular displacement between two nodes resembles their topological proximity. The hyperbolic circle ai...

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Detalles Bibliográficos
Autores principales: Muscoloni, Alessandro, Thomas, Josephine Maria, Ciucci, Sara, Bianconi, Ginestra, Cannistraci, Carlo Vittorio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2017
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5694768/
https://www.ncbi.nlm.nih.gov/pubmed/29151574
http://dx.doi.org/10.1038/s41467-017-01825-5

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5694768/
https://www.ncbi.nlm.nih.gov/pubmed/29151574
http://dx.doi.org/10.1038/s41467-017-01825-5

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