. "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\u2019s 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." . . . . "http://link.springer.com/chapter/10.1007%2F978-3-319-08156-4_35" . . "Comparison of Local and Global Ranking in Networks" . "10"^^ . "Zehnalov\u00E1, \u0160\u00E1rka" . "2194-5357" . "Comparison of Local and Global Ranking in Networks"@en . . . "3"^^ . "000342841800035" . . "27740" . "Advances in Intelligent Systems and Computing. Volume 303" . "Berlin Heidelberg" . . "RIV/61989100:27740/14:86092439" . "Sn\u00E1\u0161el, V\u00E1clav" . . "Springer-Verlag. (Berlin; Heidelberg)" . . "10.1007/978-3-319-08156-4_35" . "Hor\u00E1k, Zden\u011Bk" . . "5"^^ . "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\u2019s 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 . . . . . "Kud\u011Blka, Milo\u0161" . "Comparison of Local and Global Ranking in Networks" . . "P(ED1.1.00/02.0070), P(EE.2.3.20.0072), S" . . "Kr\u00F6mer, Pavel" . "[30B7E0B34E52]" . "Comparison of Local and Global Ranking in Networks"@en . "2014-06-23+02:00"^^ . . . . "ranking; node weighting; graphs; dependency; complex networks"@en . "978-3-319-08155-7" . . "Ostrava" . "8113" . "RIV/61989100:27740/14:86092439!RIV15-MSM-27740___" .