Statistical Mechanics of Random Hyperbolic Graphs within the Fermionic Maximum-Entropy Framework

TL;DR

This paper reviews and consolidates the statistical mechanics framework for hyperbolic random graphs, highlighting their structural properties, phase transitions, and the maximum-entropy approach as a powerful tool for analyzing complex networks.

Contribution

It unifies scattered derivations into a comprehensive framework, applying maximum-entropy principles to hyperbolic random graphs within statistical mechanics.

Findings

Hyperbolic models capture key network properties like sparsity and hierarchy.

Phase transition between geometric and non-geometric phases depends on temperature.

Fermionic particle analogy provides a novel perspective on link formation.

Abstract

The intricate relations between elements in natural and human-made systems sustain the complex processes that shape our world, forming multiscale networks of interactions. These networks can be represented as graphs composed of nodes connected by links and, regardless of their domain, they share a set of fundamental structural properties. The family of network models in hyperbolic space constitutes one of the most advanced frameworks accounting for such properties, including sparsity, the small-world property, heterogeneity and hierarchical organization, high clustering, and scale invariance under network renormalization transformations. These geometric models also exhibit other intriguing phenomena, such as an anomalous, temperature-dependent phase transition between a geometric and a non-geometric phase. In simple graph representations, where network links are unweighted, the model can be derived within a statistical-mechanics framework by maximizing the Gibbs entropy of the graph ensemble subject to constraints imposed by observations, with links effectively behaving as fermionic particles. In this topical review, I revisit these derivations previously scattered across different sources and complement them, in order to properly contextualize and consolidate hyperbolic random graphs within the broad framework of the maximum-entropy principle in the statistical mechanics of complex networks. The approach presented here represents the least-biased prediction of the fundamental set of core network properties and establishes a principled framework for analyzing network structure, offering new perspectives and powerful analytical tools for both theoretical and empirical studies.