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Description
  • Many real world data and processes have a network structure and can usefully be represented as graphs. Network analysis focuses on the relations among the nodes exploring the properties of each network. Latest trend in analyzing networks is to focus on local methods and parallelization. We introduce a method to find the ranking of the nodes. The approach extracts dependency relations among the network’s nodes. Key technical parameter of the approach is locality. Since only the surrounding of examined nodes is used in computations, there is no need to analyze the entire network. We compare this proposed local ranking to the global ranking of PageRank. We present experiment using large-scale artificial and real world networks. The results of experiment show high effectiveness due to the locality of our approach and also high quality of node ranking comparable to PageRank.
  • Many real world data and processes have a network structure and can usefully be represented as graphs. Network analysis focuses on the relations among the nodes exploring the properties of each network. Latest trend in analyzing networks is to focus on local methods and parallelization. We introduce a method to find the ranking of the nodes. The approach extracts dependency relations among the network’s nodes. Key technical parameter of the approach is locality. Since only the surrounding of examined nodes is used in computations, there is no need to analyze the entire network. We compare this proposed local ranking to the global ranking of PageRank. We present experiment using large-scale artificial and real world networks. The results of experiment show high effectiveness due to the locality of our approach and also high quality of node ranking comparable to PageRank. (en)
Title
  • Comparison of Local and Global Ranking in Networks
  • Comparison of Local and Global Ranking in Networks (en)
skos:prefLabel
  • Comparison of Local and Global Ranking in Networks
  • Comparison of Local and Global Ranking in Networks (en)
skos:notation
  • RIV/61989100:27740/14:86092439!RIV15-MSM-27740___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(ED1.1.00/02.0070), P(EE.2.3.20.0072), S
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 8113
http://linked.open...ai/riv/idVysledku
  • RIV/61989100:27740/14:86092439
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • ranking; node weighting; graphs; dependency; complex networks (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [30B7E0B34E52]
http://linked.open...v/mistoKonaniAkce
  • Ostrava
http://linked.open...i/riv/mistoVydani
  • Berlin Heidelberg
http://linked.open...i/riv/nazevZdroje
  • Advances in Intelligent Systems and Computing. Volume 303
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Horák, Zdeněk
  • Krömer, Pavel
  • Snášel, Václav
  • Kudělka, Miloš
  • Zehnalová, Šárka
http://linked.open...vavai/riv/typAkce
http://linked.open...ain/vavai/riv/wos
  • 000342841800035
http://linked.open.../riv/zahajeniAkce
issn
  • 2194-5357
number of pages
http://bibframe.org/vocab/doi
  • 10.1007/978-3-319-08156-4_35
http://purl.org/ne...btex#hasPublisher
  • Springer-Verlag. (Berlin; Heidelberg)
https://schema.org/isbn
  • 978-3-319-08155-7
http://localhost/t...ganizacniJednotka
  • 27740
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