The gravity of an edge

Abstract ■■■ 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-G...

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Main Authors: Mary E. Helander, Sarah McAllister
Format: Article
Language:English
Published: SpringerOpen 2018-05-01
Series:Applied Network Science
Subjects:
Online Access:http://link.springer.com/article/10.1007/s41109-018-0063-6
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author Mary E. Helander
Sarah McAllister
author_facet Mary E. Helander
Sarah McAllister
author_sort Mary E. Helander
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description Abstract ■■■ 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 doaj.art-0927891f89974d41a7c6b617140c52e12022-12-22T01:59:24ZengSpringerOpenApplied Network Science2364-82282018-05-013113510.1007/s41109-018-0063-6The gravity of an edgeMary E. Helander0Sarah McAllister1IBM T. J. Watson Research Center, Applied Data ScienceCUNY Borough of Manhattan Community College, Department of MathematicsAbstract ■■■ 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.http://link.springer.com/article/10.1007/s41109-018-0063-6Bridges to nowhereEdge betweennessk-shortest path (KSP)Edge Gravityk-GravityNode centrality
spellingShingle Mary E. Helander
Sarah McAllister
The gravity of an edge
Applied Network Science
Bridges to nowhere
Edge betweenness
k-shortest path (KSP)
Edge Gravity
k-Gravity
Node centrality
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 Bridges to nowhere
Edge betweenness
k-shortest path (KSP)
Edge Gravity
k-Gravity
Node centrality
url http://link.springer.com/article/10.1007/s41109-018-0063-6
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