Community Detection in Complex Networks Using Algorithms Based on K-Means and Entropy
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F20%3A10246985" target="_blank" >RIV/61989100:27240/20:10246985 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007/978-3-030-63007-2_19" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-030-63007-2_19</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-63007-2_19" target="_blank" >10.1007/978-3-030-63007-2_19</a>
Alternative languages
Result language
angličtina
Original language name
Community Detection in Complex Networks Using Algorithms Based on K-Means and Entropy
Original language description
Detecting community structures in complex networks such as social networks, computer networks, citation networks, etc. is one of the most interesting topics to many researchers, there are many works focus on this research area recently. However, the biggest difficulty is how to detect the number of complex network communities, the accuracy of the algorithms and the diversity in the properties of each complex network. In this paper, we propose an algorithm to detect the structure of communities in a complex network based on K-means algorithm and Entropy. Moreover, we also evaluated our algorithm on real-work and computer generate datasets, the results show that our approach is better than the others. (C) 2020, Springer Nature Switzerland AG.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2020
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 12496
ISBN
978-3-030-63006-5
ISSN
0302-9743
e-ISSN
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Number of pages
11
Pages from-to
241-251
Publisher name
Springer
Place of publication
Cham
Event location
Danang
Event date
Nov 30, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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