Hypernetwork science via high-order hypergraph walks
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 compone...
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| Published in | EPJ data science Vol. 9; no. 1; pp. 16 - 34 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
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Berlin/Heidelberg
Springer Berlin Heidelberg
10.06.2020
Springer Nature B.V Springer SpringerOpen |
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| Abstract | 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. |
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| AbstractList | 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. Abstract 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. Abstract 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. |
| ArticleNumber | 16 |
| Author | Joslyn, Cliff Ortiz Marrero, Carlos Praggastis, Brenda Aksoy, Sinan G. Purvine, Emilie |
| Author_xml | – sequence: 1 givenname: Sinan G. orcidid: 0000-0002-3466-3334 surname: Aksoy fullname: Aksoy, Sinan G. email: sinan.aksoy@pnnl.gov organization: Pacific Northwest National Laboratory – sequence: 2 givenname: Cliff orcidid: 0000-0002-5923-5547 surname: Joslyn fullname: Joslyn, Cliff organization: Pacific Northwest National Laboratory – sequence: 3 givenname: Carlos orcidid: 0000-0001-8302-1322 surname: Ortiz Marrero fullname: Ortiz Marrero, Carlos organization: Pacific Northwest National Laboratory – sequence: 4 givenname: Brenda orcidid: 0000-0003-1344-0497 surname: Praggastis fullname: Praggastis, Brenda organization: Pacific Northwest National Laboratory – sequence: 5 givenname: Emilie orcidid: 0000-0003-2069-5594 surname: Purvine fullname: Purvine, Emilie organization: Pacific Northwest National Laboratory |
| BackLink | https://www.osti.gov/servlets/purl/1642415$$D View this record in Osti.gov |
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| Snippet | We propose high-order hypergraph walks as a framework to generalize graph-based network science techniques to hypergraphs. Edge incidence in hypergraphs is... Abstract We propose high-order hypergraph walks as a framework to generalize graph-based network science techniques to hypergraphs. Edge incidence in... Abstract We propose high-order hypergraph walks as a framework to generalize graph-based network science techniques to hypergraphs. Edge incidence in... |
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| SubjectTerms | Clustering Co authorship Complexity Computer Appl. in Social and Behavioral Sciences Computer Science Data science Data-driven Science Generative model Graph representations Graph theory High-order walk Hypergraph hypergraphs, network analysis, generative model, hypergraph models MATHEMATICS AND COMPUTING Modeling and Theory Building Regular Article Structured data |
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| Title | Hypernetwork science via high-order hypergraph walks |
| URI | https://link.springer.com/article/10.1140/epjds/s13688-020-00231-0 https://www.proquest.com/docview/3059109556 https://www.osti.gov/servlets/purl/1642415 https://doaj.org/article/37ca1d86fecc49409e4722e1bafaa0ba |
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