A complex network model for a society with socioeconomic classes

A. N. Licciardi Jr.; L. H. A. Monteiro; A. N. Licciardi Jr.; L. H. A. Monteiro

doi:10.3934/mbe.2022317

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 7: 6731-6742. doi: 10.3934/mbe.2022317

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A complex network model for a society with socioeconomic classes

A. N. Licciardi Jr. ^1,2,
L. H. A. Monteiro ^{1,2
,
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1.
Universidade de São Paulo, Escola Politécnica, São Paulo, SP, Brazil
2.
Universidade Presbiteriana Mackenzie, Escola de Engenharia, São Paulo, SP, Brazil

Received: 21 March 2022 Revised: 18 April 2022 Accepted: 26 April 2022 Published: 04 May 2022

People's attitudes and behaviors are partially shaped by the socioeconomic class to which they belong. In this work, a model of scale-free graph is proposed to represent the daily personal contacts in a society with three social classes. In the model, the probability of having a connection between two individuals depends on their social classes and on their physical distance. Numerical simulations are performed by considering sociodemographic data from France, Peru, and Zimbabwe. For the complex networks built for these three countries, average values of node degree, shortest-path length, clustering coefficient, closeness centrality, betweenness centrality, and eigenvector centrality are computed. These numerical results are discussed by taking into account the propagation of information about COVID-19.
- centrality measures,
- complex network,
- scale-free graph,
- socioeconomic class
Citation: A. N. Licciardi Jr., L. H. A. Monteiro. A complex network model for a society with socioeconomic classes[J]. Mathematical Biosciences and Engineering, 2022, 19(7): 6731-6742. doi: 10.3934/mbe.2022317

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Abstract

People's attitudes and behaviors are partially shaped by the socioeconomic class to which they belong. In this work, a model of scale-free graph is proposed to represent the daily personal contacts in a society with three social classes. In the model, the probability of having a connection between two individuals depends on their social classes and on their physical distance. Numerical simulations are performed by considering sociodemographic data from France, Peru, and Zimbabwe. For the complex networks built for these three countries, average values of node degree, shortest-path length, clustering coefficient, closeness centrality, betweenness centrality, and eigenvector centrality are computed. These numerical results are discussed by taking into account the propagation of information about COVID-19.

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