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Statements

Subject Item
n2:RIV%2F61989100%3A27740%2F14%3A86092439%21RIV15-MSM-27740___
rdf:type
n13:Vysledek skos:Concept
rdfs:seeAlso
http://link.springer.com/chapter/10.1007%2F978-3-319-08156-4_35
dcterms: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.
dcterms:title
Comparison of Local and Global Ranking in Networks Comparison of Local and Global Ranking in Networks
skos:prefLabel
Comparison of Local and Global Ranking in Networks Comparison of Local and Global Ranking in Networks
skos:notation
RIV/61989100:27740/14:86092439!RIV15-MSM-27740___
n3:aktivita
n21:S n21:P
n3:aktivity
P(ED1.1.00/02.0070), P(EE.2.3.20.0072), S
n3:dodaniDat
n4:2015
n3:domaciTvurceVysledku
n10:7845782 n10:9175970 n10:4347269
n3:druhVysledku
n7:D
n3:duvernostUdaju
n18:S
n3:entitaPredkladatele
n15:predkladatel
n3:idSjednocenehoVysledku
8113
n3:idVysledku
RIV/61989100:27740/14:86092439
n3:jazykVysledku
n6:eng
n3:klicovaSlova
ranking; node weighting; graphs; dependency; complex networks
n3:klicoveSlovo
n8:graphs n8:ranking n8:node%20weighting n8:dependency n8:complex%20networks
n3:kontrolniKodProRIV
[30B7E0B34E52]
n3:mistoKonaniAkce
Ostrava
n3:mistoVydani
Berlin Heidelberg
n3:nazevZdroje
Advances in Intelligent Systems and Computing. Volume 303
n3:obor
n20:IN
n3:pocetDomacichTvurcuVysledku
3
n3:pocetTvurcuVysledku
5
n3:projekt
n17:ED1.1.00%2F02.0070 n17:EE.2.3.20.0072
n3:rokUplatneniVysledku
n4:2014
n3:tvurceVysledku
Zehnalová, Šárka Snášel, Václav Horák, Zdeněk Kudělka, Miloš Krömer, Pavel
n3:typAkce
n22:WRD
n3:wos
000342841800035
n3:zahajeniAkce
2014-06-23+02:00
s:issn
2194-5357
s:numberOfPages
10
n16:doi
10.1007/978-3-319-08156-4_35
n14:hasPublisher
Springer-Verlag. (Berlin; Heidelberg)
n23:isbn
978-3-319-08155-7
n12:organizacniJednotka
27740