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Hypergraph reconstruction from network data
Title / Series / Name
Publication Volume
Publication Issue
Pages
Editors
Keywords
General Physics and Astronomy
URI
https://hdl.handle.net/20.500.14018/27393
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.
Topic
Publisher
Place of Publication
Type
Journal article
Date
2021-12
Language
ISBN
Identifiers
10.1038/s42005-021-00637-w