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Inference of hyperedges and overlapping communities in hypergraphs
Title / Series / Name
Publication Volume
Publication Issue
Pages
Editors
Keywords
General Chemistry
General Biochemistry,Genetics and Molecular Biology
Multidisciplinary
General Physics and Astronomy
General Biochemistry,Genetics and Molecular Biology
Multidisciplinary
General Physics and Astronomy
URI
https://hdl.handle.net/20.500.14018/26708
Abstract
Hypergraphs, encoding structured interactions among any number of system units, have recently proven a successful tool to describe many real-world biological and social networks. Here we propose a framework based on statistical inference to characterize the structural organization of hypergraphs. The method allows to infer missing hyperedges of any size in a principled way, and to jointly detect overlapping communities in presence of higher-order interactions. Furthermore, our model has an efficient numerical implementation, and it runs faster than dyadic algorithms on pairwise records projected from higher-order data. We apply our method to a variety of real-world systems, showing strong performance in hyperedge prediction tasks, detecting communities well aligned with the information carried by interactions, and robustness against addition of noisy hyperedges. Our approach illustrates the fundamental advantages of a hypergraph probabilistic model when modeling relational systems with higher-order interactions.
Topic
Publisher
Place of Publication
Type
Journal article
Date
2022-11-24
Language
ISBN
Identifiers
10.1038/s41467-022-34714-7