Community Detection in Complex Networks Using Algorithms Based on K-Means and Entropy

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Community Detection in Complex Networks Using Algorithms Based on K-Means and Entropy

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Community Detection in Complex Networks Using Algorithms Based on K-Means and Entropy

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2020

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 12496

ISBN

978-3-030-63006-5

ISSN

0302-9743

e-ISSN

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Počet stran výsledku

11

Strana od-do

241-251

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Danang

Datum konání akce

30. 11. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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