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The gravity of an edge

We describe a methodology for characterizing the relative structural importance of an arbitrary network edge by exploiting the properties of a k-shortest path algorithm. We introduce the metric Edge Gravity, measuring how often an edge occurs in any possible network path, as well as k-Gravity, a low...

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Detalles Bibliográficos
Autores principales: Helander, Mary E., McAllister, Sarah
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6214288/
https://www.ncbi.nlm.nih.gov/pubmed/30839803
http://dx.doi.org/10.1007/s41109-018-0063-6
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author Helander, Mary E.
McAllister, Sarah
author_facet Helander, Mary E.
McAllister, Sarah
author_sort Helander, Mary E.
collection PubMed
description We describe a methodology for characterizing the relative structural importance of an arbitrary network edge by exploiting the properties of a k-shortest path algorithm. We introduce the metric Edge Gravity, measuring how often an edge occurs in any possible network path, as well as k-Gravity, a lower bound based on paths enumerated while solving the k-shortest path problem. The methodology is demonstrated using Granovetter’s original strength of weak ties network examples as well as the well-known Florentine families of the Italian Renaissance and the Krebs 2001 terrorist networks. The relationship to edge betweenness is established. It is shown that important edges, i.e. ones with a high Edge Gravity, are not necessarily adjacent to nodes of importance as identified by standard centrality metrics, and that key nodes, i.e. ones with high centrality, often have their importance bolstered by being adjacent to bridges to nowhere–e.g. ones with low Edge Gravity. It is also demonstrated that Edge Gravity distinguishes critically important bridges or local bridges from those of lesser structural importance.
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spelling pubmed-62142882018-11-13 The gravity of an edge Helander, Mary E. McAllister, Sarah Appl Netw Sci Research We describe a methodology for characterizing the relative structural importance of an arbitrary network edge by exploiting the properties of a k-shortest path algorithm. We introduce the metric Edge Gravity, measuring how often an edge occurs in any possible network path, as well as k-Gravity, a lower bound based on paths enumerated while solving the k-shortest path problem. The methodology is demonstrated using Granovetter’s original strength of weak ties network examples as well as the well-known Florentine families of the Italian Renaissance and the Krebs 2001 terrorist networks. The relationship to edge betweenness is established. It is shown that important edges, i.e. ones with a high Edge Gravity, are not necessarily adjacent to nodes of importance as identified by standard centrality metrics, and that key nodes, i.e. ones with high centrality, often have their importance bolstered by being adjacent to bridges to nowhere–e.g. ones with low Edge Gravity. It is also demonstrated that Edge Gravity distinguishes critically important bridges or local bridges from those of lesser structural importance. Springer International Publishing 2018-05-10 2018 /pmc/articles/PMC6214288/ /pubmed/30839803 http://dx.doi.org/10.1007/s41109-018-0063-6 Text en © The Author(s) 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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.
spellingShingle Research
Helander, Mary E.
McAllister, Sarah
The gravity of an edge
title The gravity of an edge
title_full The gravity of an edge
title_fullStr The gravity of an edge
title_full_unstemmed The gravity of an edge
title_short The gravity of an edge
title_sort gravity of an edge
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6214288/
https://www.ncbi.nlm.nih.gov/pubmed/30839803
http://dx.doi.org/10.1007/s41109-018-0063-6
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