Comparison of Local and Global Ranking in Networks

被引：0

作者：

Zehnalova, Sarka ^{[1
]}

Kudelka, Milos ^{[1
]}

Horak, Zdenek ^{[2
]}

Kroemer, Pavel ^{[1
]}

Snasel, Vaclav ^{[1
]}

机构：

[1] Tech Univ Ostrava, VSB, Ostrava, Czech Republic

[2] Inflex Sro, Ostrava, Czech Republic

来源：

PROCEEDINGS OF THE FIFTH INTERNATIONAL CONFERENCE ON INNOVATIONS IN BIO-INSPIRED COMPUTING AND APPLICATIONS (IBICA 2014) | 2014年 / 303卷

关键词：

complex networks; graphs; node weighting; dependency; ranking; CENTRALITY;

D O I：

10.1007/978-3-319-08156-4_35

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：355 / 364

页数：10

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