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Annotated hypergraphs: models and applications

Author(s)
Chodrow, Philip Samuel; Mellor, Andrew
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Abstract
Hypergraphs offer a natural modeling language for studying polyadic interactions between sets of entities. Many polyadic interactions are asymmetric, with nodes playing distinctive roles. In an academic collaboration network, for example, the order of authors on a paper often reflects the nature of their contributions to the completed work. To model these networks, we introduce annotated hypergraphs as natural polyadic generalizations of directed graphs. Annotated hypergraphs form a highly general framework for incorporating metadata into polyadic graph models. To facilitate data analysis with annotated hypergraphs, we construct a role-aware configuration null model for these structures and prove an efficient Markov Chain Monte Carlo scheme for sampling from it. We proceed to formulate several metrics and algorithms for the analysis of annotated hypergraphs. Several of these, such as assortativity and modularity, naturally generalize dyadic counterparts. Other metrics, such as local role densities, are unique to the setting of annotated hypergraphs. We illustrate our techniques on six digital social networks, and present a detailed case-study of the Enron email data set. Keywords: Hypergraphs; Null models;l Network science; Statistical inference; Community detection
Date issued
2020-01
URI
https://hdl.handle.net/1721.1/124666
Department
Massachusetts Institute of Technology. Operations Research Center
Journal
Applied Network Science
Publisher
Springer Science and Business Media LLC
Citation
Chodrow, Philip, and Andrew Mellor. “Annotated Hypergraphs: Models and Applications.” Applied Network Science 5, 1 (December 2020): 9. © 2020 The Authors
Version: Final published version
ISSN
2364-8228

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