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dc.contributor.authorZhang, Ronda J.en_US
dc.contributor.authorStanley, H. Eugeneen_US
dc.contributor.authorYe, Fred Y.en_US
dc.date.accessioned2020-02-27T20:53:14Z
dc.date.available2020-02-27T20:53:14Z
dc.date.issued2018-09-25
dc.identifierhttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000445572100015&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=6e74115fe3da270499c3d65c9b17d654
dc.identifier.citationRonda J Zhang, H Eugene Stanley, Fred Y Ye. 2018. "Extracting h-Backbone as a Core Structure in Weighted Networks." Scientific Reports, Volume 8. https://doi.org/10.1038/s41598-018-32430-1
dc.identifier.issn2045-2322
dc.identifier.urihttps://hdl.handle.net/2144/39580
dc.description.abstractDetermining the core structure of complex network systems allows us to simplify them. Using h-bridge and h-strength measurements in a weighted network, we extract the h-backbone core structure. We find that focusing on the h-backbone in a network allows greater simplification because it has fewer edges and thus fewer adjacent nodes. We examine three practical applications: the co-citation network in an information system, the open flight network in a social system, and coauthorship in network science publications.en_US
dc.description.sponsorshipWe acknowledge the financial support from the National Natural Science Foundation of China Grant No. 71673131. The Boston University Center for Polymer Studies is supported by NSF Grants PHY-1505000, CMMI-1125290, and CHE-1213217, by DTRA Grant HDTRA1-14-1-0017, and by DOE Contract DE-AC07-05Id14517. (71673131 - National Natural Science Foundation of China; PHY-1505000 - NSF; CMMI-1125290 - NSF; CHE-1213217 - NSF; HDTRA1-14-1-0017 - DTRA; DE-AC07-05Id14517 - DOE)en_US
dc.format.extent7 pagesen_US
dc.languageEnglish
dc.publisherNATURE PUBLISHING GROUPen_US
dc.relation.ispartofSCIENTIFIC REPORTS
dc.rights© The Author(s) 2018. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.en_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectScience & technologyen_US
dc.subjectMultidisciplinary sciencesen_US
dc.subjectComplex networksen_US
dc.subjectBiochemistry and cell biologyen_US
dc.subjectNetworksen_US
dc.titleExtracting h-backbone as a core structure in weighted networksen_US
dc.typeArticleen_US
dc.description.versionPublished versionen_US
dc.identifier.doi10.1038/s41598-018-32430-1
pubs.elements-sourceweb-of-scienceen_US
pubs.notesEmbargo: Not knownen_US
pubs.organisational-groupBoston Universityen_US
pubs.organisational-groupBoston University, College of Arts & Sciencesen_US
pubs.organisational-groupBoston University, College of Arts & Sciences, Department of Physicsen_US
pubs.publication-statusPublisheden_US
dc.identifier.mycv399188


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© The Author(s) 2018. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
Except where otherwise noted, this item's license is described as © The Author(s) 2018. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.