Search in weighted complex networks

Hari P. Thadakamalla; R. Albert; S. R.T. Kumara

doi:10.1103/PhysRevE.72.066128

Search in weighted complex networks

Hari P. Thadakamalla, R. Albert, S. R.T. Kumara

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49 Scopus citations

Abstract

We study trade-offs presented by local search algorithms in complex networks which are heterogeneous in edge weights and node degree. We show that search based on a network measure, local betweenness centrality (LBC), utilizes the heterogeneity of both node degrees and edge weights to perform the best in scale-free weighted networks. The search based on LBC is universal and performs well in a large class of complex networks.

Original language	English (US)
Article number	066128
Journal	Physical Review E - Statistical, Nonlinear, and Soft Matter Physics
Volume	72
Issue number	6
DOIs	https://doi.org/10.1103/PhysRevE.72.066128
State	Published - Dec 2005

All Science Journal Classification (ASJC) codes

Statistical and Nonlinear Physics
Statistics and Probability
Condensed Matter Physics

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10.1103/PhysRevE.72.066128

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title = "Search in weighted complex networks",

abstract = "We study trade-offs presented by local search algorithms in complex networks which are heterogeneous in edge weights and node degree. We show that search based on a network measure, local betweenness centrality (LBC), utilizes the heterogeneity of both node degrees and edge weights to perform the best in scale-free weighted networks. The search based on LBC is universal and performs well in a large class of complex networks.",

author = "Thadakamalla, {Hari P.} and R. Albert and Kumara, {S. R.T.}",

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N2 - We study trade-offs presented by local search algorithms in complex networks which are heterogeneous in edge weights and node degree. We show that search based on a network measure, local betweenness centrality (LBC), utilizes the heterogeneity of both node degrees and edge weights to perform the best in scale-free weighted networks. The search based on LBC is universal and performs well in a large class of complex networks.

AB - We study trade-offs presented by local search algorithms in complex networks which are heterogeneous in edge weights and node degree. We show that search based on a network measure, local betweenness centrality (LBC), utilizes the heterogeneity of both node degrees and edge weights to perform the best in scale-free weighted networks. The search based on LBC is universal and performs well in a large class of complex networks.

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Search in weighted complex networks

Abstract

All Science Journal Classification (ASJC) codes

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