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Unfolding the multiscale structure of networks with dynamical Ollivier-Ricci curvature
Describing networks geometrically through low-dimensional latent metric spaces has helped design efficient learning algorithms, unveil network symmetries and study dynamical network processes. However, latent space embeddings are limited to specific classes of networks because incompatible metric sp...
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| Publicado no: | Nat Commun |
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| Main Authors: | , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
Nature Publishing Group UK
2021
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC8316456/ https://ncbi.nlm.nih.gov/pubmed/34315911 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1038/s41467-021-24884-1 |
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