Hypernetwork science via high-order hypergraph walks

Aksoy, Sinan G.; Joslyn, Cliff; Ortiz Marrero, Carlos; Praggastis, Brenda; Purvine, Emilie

doi:10.1140/epjds/s13688-020-00231-0

Hypernetwork science via high-order hypergraph walks

Journal Article · Wed Jun 10 00:00:00 EDT 2020 · EPJ Data Science

DOI:https://doi.org/10.1140/epjds/s13688-020-00231-0· OSTI ID:1642415

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Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Pacific Northwest National Lab. (PNNL), Seattle, WA (United States)

We propose high-order hypergraph walks as a framework to generalize graph-based network science techniques to hypergraphs. Edge incidence in hypergraphs is quantitative, yielding hypergraph walks with both length and width. Graph methods which then generalize to hypergraphs include connected component analyses, graph distance-based metrics such as closeness centrality, and motif-based measures such as clustering coefficients. We apply high-order analogs of these methods to real world hypernetworks, and show they reveal nuanced and interpretable structure that cannot be detected by graph-based methods. Lastly, we apply three generative models to the data and find that basic hypergraph properties, such as density and degree distributions, do not necessarily control these new structural measurements. Our work demonstrates how analyses of hypergraph-structured data are richer when utilizing tools tailored to capture hypergraph-native phenomena, and suggests one possible avenue towards that end.

View Accepted Manuscript (DOE)

Research Organization:: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: AC05-76RL01830

OSTI ID:: 1642415

Report Number(s):: PNNL-SA--144766

Journal Information:: EPJ Data Science, Journal Name: EPJ Data Science Journal Issue: 1 Vol. 9; ISSN 2193-1127

Publisher:: SpringerCopyright Statement

Country of Publication:: United States

Language:: English

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