Local Dependency in Networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F15%3A86096673" target="_blank" >RIV/61989100:27240/15:86096673 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27740/15:86096673

Výsledek na webu

<a href="http://dx.doi.org/10.1515/amcs-2015-0022" target="_blank" >http://dx.doi.org/10.1515/amcs-2015-0022</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1515/amcs-2015-0022" target="_blank" >10.1515/amcs-2015-0022</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Local Dependency in Networks

Popis výsledku v původním jazyce

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. We introduce a method for measuring the strength ofthe relationship between two nodes of a network and for their ranking. This method is applicable to all kinds of networks, including directed and weighted networks. The approach extracts dependency relations among the network's nodes from the structurein local surroundings of individual nodes. For the tasks we deal with in this article, the key technical parameter is locality. Since only the surroundings of the examined nodes are used in computations, there is no need to analyze the entire network. This allows the application of our approach in the area of large-scale networks. We present several experiments using small networks as well as large-scale artificial and real world networks. The results of the experiments show high effecti

Název v anglickém jazyce

Local Dependency in Networks

Popis výsledku anglicky

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. We introduce a method for measuring the strength ofthe relationship between two nodes of a network and for their ranking. This method is applicable to all kinds of networks, including directed and weighted networks. The approach extracts dependency relations among the network's nodes from the structurein local surroundings of individual nodes. For the tasks we deal with in this article, the key technical parameter is locality. Since only the surroundings of the examined nodes are used in computations, there is no need to analyze the entire network. This allows the application of our approach in the area of large-scale networks. We present several experiments using small networks as well as large-scale artificial and real world networks. The results of the experiments show high effecti

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

<a href="/cs/project/ED1.1.00%2F02.0070" target="_blank" >ED1.1.00/02.0070: Centrum excelence IT4Innovations</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

International Journal of Applied Mathematics and Computer Science

ISSN

1641-876X

e-ISSN

—

Svazek periodika

25

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

PL - Polská republika

Počet stran výsledku

13

Strana od-do

281-293

Kód UT WoS článku

000358017900008

EID výsledku v databázi Scopus

2-s2.0-84934268517