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Structure of complex networks: Quantifying edge-to-edge relations by failure-induced flow redistribution

Published online by Cambridge University Press:  03 April 2014

MICHAEL T. SCHAUB ,
JÖRG LEHMANN ,
SOPHIA N. YALIRAKI  and
MAURICIO BARAHONA Open the ORCID record for MAURICIO BARAHONA [Opens in a new window]
MICHAEL T. SCHAUB
Affiliation:
Department of Mathematics, Imperial College London, South Kensington Campus, London SW7 2AZ, UK (e-mail: michael.schaub09@imperial.ac.uk)
JÖRG LEHMANN
Affiliation:
ABB Switzerland Ltd, Corporate Research, CH-5405 Baden-Dättwil, Switzerland (e-mail: joerg.lehmann@ch.abb.com)
SOPHIA N. YALIRAKI
Affiliation:
Department of Chemistry, Imperial College London, South Kensington Campus, London SW7 2AZ, UK (e-mail: s.yaliraki@imperial.ac.uk)
MAURICIO BARAHONA
Affiliation:
Department of Mathematics, Imperial College London, South Kensington Campus, London SW7 2AZ, UK (e-mail: m.barahona@imperial.ac.uk)
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Abstract

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The analysis of complex networks has so far revolved mainly around the role of nodes and communities of nodes. However, the dynamics of interconnected systems is often focalized on edge processes, and a dual edge-centric perspective can often prove more natural. Here we present graph-theoretical measures to quantify edge-to-edge relations inspired by the notion of flow redistribution induced by edge failures. Our measures, which are related to the pseudo-inverse of the Laplacian of the network, are global and reveal the dynamical interplay between the edges of a network, including potentially non-local interactions. Our framework also allows us to define the embeddedness of an edge, a measure of how strongly an edge features in the weighted cuts of the network. We showcase the general applicability of our edge-centric framework through analyses of the Iberian power grid, traffic flow in road networks, and the C. elegans neuronal network.

Keywords

flow redistributionedge-to-edge relationspseudoinverse of the Laplacianedge-failurespower gridstraffic networksC. elegansedge-centralitiescommunity structure

Information

Type
Research Article
Information
Network Science , Volume 2 , Issue 1 , April 2014 , pp. 66 - 89
Copyright
Copyright © Cambridge University Press 2014 

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