Representativeness in unweighted networks based on local dependency

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F14%3A86096684" target="_blank" >RIV/61989100:27240/14:86096684 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27740/14:86096684

Výsledek na webu

<a href="http://dx.doi.org/10.1109/INCoS.2014.68" target="_blank" >http://dx.doi.org/10.1109/INCoS.2014.68</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/INCoS.2014.68" target="_blank" >10.1109/INCoS.2014.68</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Representativeness in unweighted networks based on local dependency

Popis výsledku v původním jazyce

Structures of real-world networks show varying degrees of importance of the nodes in their surroundings. The topic of evaluating the importance of the nodes offers many different approaches. We present simple and straightforward approach for the evaluation of the nodes in undirected unweighted networks. The approach is based on x-representativeness measure which is originally intended for weighted networks. The x-representativeness takes into account the degree of the node and its nearest neighbors. Experiments with different real-world unweighted networks are presented. To apply the presented method it is necessary to transform undirected unweighted network into weighted network. Weights in our experiments are measured by dependency between adjacent nodes. The aim of these experiments is to show that the x-representativeness can be used to deterministically reduce the unweighted network to differently sized samples of representatives, while maintaining topological properties of the or

Název v anglickém jazyce

Representativeness in unweighted networks based on local dependency

Popis výsledku anglicky

Structures of real-world networks show varying degrees of importance of the nodes in their surroundings. The topic of evaluating the importance of the nodes offers many different approaches. We present simple and straightforward approach for the evaluation of the nodes in undirected unweighted networks. The approach is based on x-representativeness measure which is originally intended for weighted networks. The x-representativeness takes into account the degree of the node and its nearest neighbors. Experiments with different real-world unweighted networks are presented. To apply the presented method it is necessary to transform undirected unweighted network into weighted network. Weights in our experiments are measured by dependency between adjacent nodes. The aim of these experiments is to show that the x-representativeness can be used to deterministically reduce the unweighted network to differently sized samples of representatives, while maintaining topological properties of the or

Druh

D - Stať ve sborníku

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

2014

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings - 2014 International Conference on Intelligent Networking and Collaborative Systems, IEEE INCoS 2014

ISBN

978-1-4799-6386-7

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

509-514

Název nakladatele

Institute of Electrical and Electronics Engineers

Místo vydání

New York

Místo konání akce

Salerno

Datum konání akce

10. 9. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—