Entropy-based models to randomise real-world hypergraphs

Saracco, Fabio, Petri, Giovanni, Lambiotte, Renaud and Squartini, Tiziano (2025) Entropy-based models to randomise real-world hypergraphs. Communications Physics, 8 (284). ISSN 2399-3650

Abstract

Network theory has often disregarded many-body relationships, solely focusing on pairwise interactions: neglecting them, however, can lead to misleading representations of complex systems. Hypergraphs represent a suitable framework for describing polyadic interactions. Here, we leverage the representation of hypergraphs based on the incidence matrix for extending the entropy-based approach to higher-order structures: in analogy with the Exponential Random Graphs, we introduce the Exponential Random Hypergraphs (ERHs). After exploring the asymptotic behaviour of thresholds generalising the percolation one, we apply ERHs to study real-world data. First, we generalise key network metrics to hypergraphs; then, we compute their expected value and compare it with the empirical one, in order to detect deviations from random behaviours. Our method is analytically tractable, scalable and capable of revealing structural patterns of real-world hypergraphs that differ significantly from those emerging as a consequence of simpler constraints.

Text s42005-025-02182-2.pdf - Published Version Available under License Creative Commons Attribution. Download (2MB)

Official URL:	https://www.nature.com/articles/s42005-025-02182-2...
Item Type:	Article
Disciplines:	Network Science
Depositing User:	Mizpah Agyapong
Date Deposited:	11 Jul 2025 09:22
Last Modified:	11 Jul 2025 09:22
URI:	https://nul.repository.guildhe.ac.uk/id/eprint/2564

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