Communities Detection in Multiplex Networks Using Optimization: Study Case—Employment in Mexico during the COVID-19 Pandemic
The detection of communities in complex networks offers important information about the structure of the network as well as its dynamics. However, it is not an easy problem to solve. This work presents a methodology based of the robust coloring problem (RCP) and the vertex cover problem (VCP) to fin...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2023/9011738 |
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| author | Edwin Montes-Orozco Roman Anselmo Mora-Gutiérrez Sergio Gerardo De-Los-Cobos-Silva Roberto Bernal-Jaquez Eric Alfredo Rincón-García Miguel Angel Gutiérrez-Andrade Pedro Lara-Velázquez |
| author_facet | Edwin Montes-Orozco Roman Anselmo Mora-Gutiérrez Sergio Gerardo De-Los-Cobos-Silva Roberto Bernal-Jaquez Eric Alfredo Rincón-García Miguel Angel Gutiérrez-Andrade Pedro Lara-Velázquez |
| author_sort | Edwin Montes-Orozco |
| collection | DOAJ |
| description | The detection of communities in complex networks offers important information about the structure of the network as well as its dynamics. However, it is not an easy problem to solve. This work presents a methodology based of the robust coloring problem (RCP) and the vertex cover problem (VCP) to find communities in multiplex networks. For this, we consider the RCP idea of having a partial detection based onf the similarity of connected and unconnected nodes. On the other hand, with the idea of the VCP, we manage to minimize the number of groups, which allows us to identify the communities well. To apply this methodology, we present the dynamic characterization of job loss, change, and acquisition behavior for the Mexican population before and during the COVID-19 pandemic modeled as a 4- layer multiplex network. The results obtained when applied to test and study case networks show that this methodology can classify elements with similar characteristics and can find their communities. Therefore, our proposed methodology can be used as a new mechanism to identify communities, regardless of the topology or whether it is a monoplex or multiplex network. |
| format | Article |
| id | doaj-art-b461376c6c7c4921aa4e0354ff82d425 |
| institution | Kabale University |
| issn | 1099-0526 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-b461376c6c7c4921aa4e0354ff82d4252025-02-03T01:30:24ZengWileyComplexity1099-05262023-01-01202310.1155/2023/9011738Communities Detection in Multiplex Networks Using Optimization: Study Case—Employment in Mexico during the COVID-19 PandemicEdwin Montes-Orozco0Roman Anselmo Mora-Gutiérrez1Sergio Gerardo De-Los-Cobos-Silva2Roberto Bernal-Jaquez3Eric Alfredo Rincón-García4Miguel Angel Gutiérrez-Andrade5Pedro Lara-Velázquez6Departamento de Matemáticas Aplicadas y SistemasDepartamento de SistemasDepartamento de Ingeniería EléctricaDepartamento de Matemáticas Aplicadas y SistemasDepartamento de Ingeniería EléctricaDepartamento de Ingeniería EléctricaDepartamento de Ingeniería EléctricaThe detection of communities in complex networks offers important information about the structure of the network as well as its dynamics. However, it is not an easy problem to solve. This work presents a methodology based of the robust coloring problem (RCP) and the vertex cover problem (VCP) to find communities in multiplex networks. For this, we consider the RCP idea of having a partial detection based onf the similarity of connected and unconnected nodes. On the other hand, with the idea of the VCP, we manage to minimize the number of groups, which allows us to identify the communities well. To apply this methodology, we present the dynamic characterization of job loss, change, and acquisition behavior for the Mexican population before and during the COVID-19 pandemic modeled as a 4- layer multiplex network. The results obtained when applied to test and study case networks show that this methodology can classify elements with similar characteristics and can find their communities. Therefore, our proposed methodology can be used as a new mechanism to identify communities, regardless of the topology or whether it is a monoplex or multiplex network.http://dx.doi.org/10.1155/2023/9011738 |
| spellingShingle | Edwin Montes-Orozco Roman Anselmo Mora-Gutiérrez Sergio Gerardo De-Los-Cobos-Silva Roberto Bernal-Jaquez Eric Alfredo Rincón-García Miguel Angel Gutiérrez-Andrade Pedro Lara-Velázquez Communities Detection in Multiplex Networks Using Optimization: Study Case—Employment in Mexico during the COVID-19 Pandemic Complexity |
| title | Communities Detection in Multiplex Networks Using Optimization: Study Case—Employment in Mexico during the COVID-19 Pandemic |
| title_full | Communities Detection in Multiplex Networks Using Optimization: Study Case—Employment in Mexico during the COVID-19 Pandemic |
| title_fullStr | Communities Detection in Multiplex Networks Using Optimization: Study Case—Employment in Mexico during the COVID-19 Pandemic |
| title_full_unstemmed | Communities Detection in Multiplex Networks Using Optimization: Study Case—Employment in Mexico during the COVID-19 Pandemic |
| title_short | Communities Detection in Multiplex Networks Using Optimization: Study Case—Employment in Mexico during the COVID-19 Pandemic |
| title_sort | communities detection in multiplex networks using optimization study case employment in mexico during the covid 19 pandemic |
| url | http://dx.doi.org/10.1155/2023/9011738 |
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