Improving node similarity for discovering community structure in complex networks
The result's identifiers
Result code in 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>
Alternative codes found
RIV/61989100:27740/16:86099089
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Improving node similarity for discovering community structure in complex networks
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
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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Number of pages
12
Pages from-to
74-85
Publisher name
Springer
Place of publication
Cham
Event location
Ho Či Minovo Město
Event date
Aug 2, 2016
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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