Improving node similarity for discovering community structure in complex networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F16%3A86099089" target="_blank" >RIV/61989100:27240/16:86099089 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27740/16:86099089

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-319-42345-6_7" target="_blank" >http://dx.doi.org/10.1007/978-3-319-42345-6_7</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-42345-6_7" target="_blank" >10.1007/978-3-319-42345-6_7</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Improving node similarity for discovering community structure in complex networks

Popis výsledku v původním jazyce

Community detection is to detect groups consisting of densely connected nodes, and having sparse connections between them. Many researchers indicate that detecting community structures in complex networks can extract plenty of useful information, such as the structural features, network properties, and dynamic characteristics of the community. Several community detection methods introduced different similarity measures between nodes, and their performance can be improved. In this paper, we propose a community detection method based on an improvement of node similarities. Our method initializes a level for each node and assigns nodes into a community based on similarity between nodes. Then it selects core communities and expands those communities by layers. Finally, we merge communities and choose the best community in the network. The experimental results show that our method achieves state-of-the-art performance. (C) Springer International Publishing Switzerland 2016.

Název v anglickém jazyce

Improving node similarity for discovering community structure in complex networks

Popis výsledku anglicky

Community detection is to detect groups consisting of densely connected nodes, and having sparse connections between them. Many researchers indicate that detecting community structures in complex networks can extract plenty of useful information, such as the structural features, network properties, and dynamic characteristics of the community. Several community detection methods introduced different similarity measures between nodes, and their performance can be improved. In this paper, we propose a community detection method based on an improvement of node similarities. Our method initializes a level for each node and assigns nodes into a community based on similarity between nodes. Then it selects core communities and expands those communities by layers. Finally, we merge communities and choose the best community in the network. The experimental results show that our method achieves state-of-the-art performance. (C) Springer International Publishing Switzerland 2016.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2016

Kód důvěrnosti údajů

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

Název statě ve sborníku

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). volume 9795

ISBN

978-3-319-42344-9

ISSN

0302-9743

e-ISSN

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Počet stran výsledku

12

Strana od-do

74-85

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Ho Či Minovo Město

Datum konání akce

2. 8. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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