Networks can describe the structure of a wide variety of complex systems by specifying how pairs of nodes interact. This choice of representation is flexible, but not necessarily appropriate when joint interactions between groups of nodes are needed to explain empirical phenomena. Networks remain the de facto standard, however, as relational datasets often fail to include higher-order interactions  . Here, we introduce a Bayesian approach to reconstruct these missing higher-order interactions , from 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  .
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 .
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how pairs of nodes interact. This choice of representation is flexible, but
which pairs of entities in the system are connected. While such pairwise representations are flexible, they are
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joint interactions between groups of nodes are needed to explain empirical phenomena. Networks remain the de facto standard, however, as relational datasets often fail to include
the fundamental interactions involve more than two entities at the same time. Pairwise representations nonetheless remain ubiquitous, because
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are often not recorded explicitly in network data
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these missing
latent
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, from
from ordinary
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. We demonstrate its applicability to a wide range of datasets, both synthetic and empirical