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

Research output: Contribution to journal › Article › peer-review

50 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.\}",

year = "2005",

month = dec,

doi = "10.1103/PhysRevE.72.066128",

language = "English (US)",

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journal = "Physical Review E - Statistical, Nonlinear, and Soft Matter Physics",

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AU - Albert, R.

AU - Kumara, S. R.T.

PY - 2005/12

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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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U2 - 10.1103/PhysRevE.72.066128

DO - 10.1103/PhysRevE.72.066128

M3 - Article

AN - SCOPUS:33244491668

SN - 1539-3755

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JO - Physical Review E - Statistical, Nonlinear, and Soft Matter Physics

JF - Physical Review E - Statistical, Nonlinear, and Soft Matter Physics

IS - 6

M1 - 066128

ER -

Search in weighted complex networks

Abstract

All Science Journal Classification (ASJC) codes

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