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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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| Publicado no: | Nat Commun |
|---|---|
| Main Authors: | , , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
Nature Publishing Group UK
2017
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC5694768/ https://ncbi.nlm.nih.gov/pubmed/29151574 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1038/s41467-017-01825-5 |
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