Hypergraph reconstruction from network data
This addresses the limitation of pairwise representations in complex systems where interactions involve multiple entities, offering a method to infer higher-order structures from common network data.
The authors tackled the problem of reconstructing latent higher-order interactions from pairwise network data, introducing a Bayesian method based on parsimony that includes higher-order structures only with sufficient statistical evidence, and demonstrated its applicability across synthetic and empirical datasets.
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.