NDLI: Spanning Edge Centrality: Large-scale Computation and Applications

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Spanning Edge Centrality: Large-scale Computation and Applications

Content Provider	ACM Digital Library
Author	Mavroforakis, Charalampos Terzi, Evimaria Koutis, Ioannis Garcia-Lebron, Richard
Abstract	The spanning centrality of an edge e in an undirected graph G is the fraction of the spanning trees of G that contain e. Despite its appealing definition and apparent value in certain applications in computational biology, spanning centrality hasn't so far received a wider attention as a measure of edge centrality. We may partially attribute this to the perceived complexity of computing it, which appears to be prohibitive for very large networks. Contrary to this intuition, spanning centrality can in fact be approximated arbitrary well by very efficient near-linear time algorithms due to Spielman and Srivastava, combined with progress in linear system solvers. In this article we bring theory into practice, with careful and optimized implementations that allow the fast computation of spanning centrality in very large graphs with millions of nodes. With this computational tool in our disposition, we demonstrate experimentally that spanning centrality is in fact a useful tool for the analysis of large networks. Specifically, we show that, relative to common centrality measures, spanning centrality is more effective in identifying edges whose removal causes a higher disruption in an information propagation procedure, while being very resilient to noise, in terms of both the edges scores and the resulting edge ranking.
Starting Page	732
Ending Page	742
Page Count	11
File Format	PDF
ISBN	9781450334693
DOI	10.1145/2736277.2741125
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-05-18
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Spanning trees Social networks Edge centrality Graph algorithms
Content Type	Text
Resource Type	Article

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