Dimension matters when modeling network communities in hyperbolic spaces

TL;DR

This paper demonstrates that increasing the latent space dimensions in hyperbolic graph models significantly improves the realism and diversity of community structures, better capturing properties of real-world networks.

Contribution

It highlights the importance of latent space dimensionality in hyperbolic models for accurately representing network communities, a factor previously overlooked.

Findings

Higher-dimensional hyperbolic models produce more realistic community structures.

Adding just one dimension enhances the diversity of generated communities.

Dimensionality influences how node similarity affects connection probabilities.

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 important qualitative difference between the lowest-dimensional model and its higher-dimensional counterparts with respect to how similarity between nodes restricts connection probabilities. Since more dimensions also increase the number of nearest neighbors for angular clusters representing communities, considering only one more dimension allows us to generate more realistic and diverse community structures.