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- W4285064427 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 emph{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." @default.
- W4285064427 created "2022-07-13" @default.
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- W4285064427 date "2019-11-04" @default.
- W4285064427 modified "2023-09-25" @default.
- W4285064427 title "Annotated Hypergraphs: Models and Applications" @default.
- W4285064427 doi "https://doi.org/10.48550/arxiv.1911.01331" @default.
- W4285064427 hasPublicationYear "2019" @default.
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