Dimension matters when modeling network communities in hyperbolic spaces.

ABSTRACT: Over the last decade, random hyperbolic graphs have proved successful in providing geometric explanations for many key properties of real-world networks, including strong clustering, high navigability, and heterogeneous degree distributions. These properties are ubiquitous in systems as varied as the internet, transportation, brain or epidemic networks, which are thus unified under the hyperbolic network interpretation on a surface of constant negative curvature. Although a few studies have shown that hyperbolic models can generate community structures, another salient feature observed in real networks, we argue that the current models are overlooking the choice of the latent space dimensionality that is required to adequately represent clustered networked data. We show that there is an im

SUBMITTER: Desy B 

PROVIDER: S-EPMC10167553 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature