A Social Network Analysis Approach to COVID-19 Community Detection Techniques.

Choudhury, Tanupriya; Arunachalam, Rohini; Khanna, Abhirup; Jasinska, Elzbieta; Bolshev, Vadim; Panchenko, Vladimir; Leonowicz, Zbigniew

Choudhury, Tanupriya; Arunachalam, Rohini; Khanna, Abhirup; Jasinska, Elzbieta; Bolshev, Vadim; Panchenko, Vladimir; Leonowicz, Zbigniew.

Afiliação

Choudhury T; Informatics Cluster, School of Computer Science, University of Petroleum and Energy Studies (UPES), Dehradun 248007, India.
Arunachalam R; Miracle Educational Society Group of Institutions, ViziaNagaram 535216, Andhra Pradesh, India.
Khanna A; Systemics Cluster, School of Computer Science, University of Petroleum and Energy Studies (UPES), Dehradun 248007, India.
Jasinska E; Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland.
Bolshev V; Federal Scientific Agroengineering Center VIM, 109428 Moscow, Russia.
Panchenko V; Federal Scientific Agroengineering Center VIM, 109428 Moscow, Russia.
Leonowicz Z; Department of Theoretical and Applied Mechanics, Russian Open Academy of Transport, 125315 Moscow, Russia.

Int J Environ Res Public Health ; 19(7)2022 03 23.

Article em En | MEDLINE | ID: mdl-35409474

Assuntos

COVID-19; Análise de Rede Social; COVID-19/epidemiologia; Análise por Conglomerados; Humanos; Índia; Aprendizado de Máquina

Palavras-chave

COVID-19 community; clustering; node metrics; social network

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Análise de Rede Social / COVID-19 Tipo de estudo: Diagnostic_studies Aspecto: Determinantes_sociais_saude Limite: Humans País/Região como assunto: Asia Idioma: En Revista: Int J Environ Res Public Health Ano de publicação: 2022 Tipo de documento: Article

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