Hypergraph reconstruction from network data

TL;DR

This paper introduces a Bayesian method to infer higher-order interactions in complex systems from standard pairwise network data, addressing the limitations of traditional pairwise network representations.

Contribution

It presents a novel Bayesian approach that reconstructs latent higher-order interactions, only including them when statistically justified, improving the analysis of complex systems.

Findings

Effective in synthetic datasets

Applicable to empirical network data

Reveals hidden higher-order structures

Abstract

Networks can describe the structure of a wide variety of complex systems by specifying which pairs of entities in the system are connected. While such pairwise representations are flexible, they are not necessarily appropriate when the fundamental interactions involve more than two entities at the same time. Pairwise representations nonetheless remain ubiquitous, because higher-order interactions are often not recorded explicitly in network data. Here, we introduce a Bayesian approach to reconstruct latent higher-order interactions from ordinary pairwise network data. Our method is based on the principle of parsimony and only includes higher-order structures when there is sufficient statistical evidence for them. We demonstrate its applicability to a wide range of datasets, both synthetic and empirical.