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dc.contributor.authorWang, Zhenghua
Dueñas-Osorio, Leonardo
Padgett, Jamie E.
dc.date.accessioned 2016-04-04T18:26:23Z
dc.date.available 2016-04-04T18:26:23Z
dc.date.issued 2015
dc.identifier.citation Wang, Zhenghua, Dueñas-Osorio, Leonardo and Padgett, Jamie E.. "A new mutually reinforcing network node and link ranking algorithm." Scientific Reports, 5, (2015) Macmillan Publishers Limited: http://dx.doi.org/10.1038/srep15141.
dc.identifier.urihttps://hdl.handle.net/1911/88829
dc.description.abstract This study proposes a novel Normalized Wide network Ranking algorithm (NWRank) that has the advantage of ranking nodes and links of a network simultaneously. This algorithm combines the mutual reinforcement feature of Hypertext Induced Topic Selection (HITS) and the weight normalization feature of PageRank. Relative weights are assigned to links based on the degree of the adjacent neighbors and the Betweenness Centrality instead of assigning the same weight to every link as assumed in PageRank. Numerical experiment results show that NWRank performs consistently better than HITS, PageRank, eigenvector centrality, and edge betweenness from the perspective of network connectivity and approximate network flow, which is also supported by comparisons with the expensive N-1 benchmark removal criteria based on network efficiency. Furthermore, it can avoid some problems, such as the Tightly Knit Community effect, which exists in HITS. NWRank provides a new inexpensive way to rank nodes and links of a network, which has practical applications, particularly to prioritize resource allocation for upgrade of hierarchical and distributed networks, as well as to support decision making in the design of networks, where node and link importance depend on a balance of local and global integrity.
dc.language.iso eng
dc.publisher Macmillan Publishers Limited
dc.rights This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material.
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.title A new mutually reinforcing network node and link ranking algorithm
dc.type Journal article
dc.contributor.funder National Science Foundation
dc.contributor.funder U.S. Department of Defense
dc.citation.journalTitle Scientific Reports
dc.citation.volumeNumber 5
dc.type.dcmi Text
dc.identifier.doihttp://dx.doi.org/10.1038/srep15141
dc.identifier.pmcid PMC4615982
dc.identifier.pmid 26492958
dc.identifier.grantID CMMI-1234690 (National Science Foundation)
dc.identifier.grantID W911NF-13-1-0340 (U.S. Department of Defense)
dc.type.publication publisher version


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This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material.
Except where otherwise noted, this item's license is described as This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material.