Normalized Sombor Indices as Complexity Measures of Random Networks

We perform a detailed computational study of the recently introduced Sombor indices on random networks. Specifically, we apply Sombor indices on three models of random networks: Erdös-Rényi networks, random geometric graphs, and bipartite random networks. Within a statistical random matrix theory ap...

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Main Authors: R. Aguilar-Sánchez, J. A. Méndez-Bermúdez, José M. Rodríguez, José M. Sigarreta
Format: Article
Language:English
Published: MDPI AG 2021-07-01
Series:Entropy
Subjects:
Online Access:https://www.mdpi.com/1099-4300/23/8/976
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spelling doaj-2b16705746e54cc48eaf4196923ec3332021-08-26T13:44:03ZengMDPI AGEntropy1099-43002021-07-012397697610.3390/e23080976Normalized Sombor Indices as Complexity Measures of Random NetworksR. Aguilar-Sánchez0J. A. Méndez-Bermúdez1José M. Rodríguez2José M. Sigarreta3Facultad de Ciencias Químicas, Benemérita Universidad Autónoma de Puebla, Puebla 72570, MexicoInstituto de Física, Benemérita Universidad Autónoma de Puebla, Apartado Postal J-48, Puebla 72570, MexicoDepartamento de Matemáticas, Universidad Carlos III de Madrid, Avenida de la Universidad 30, 28911 Leganés, Madrid, SpainFacultad de Matemáticas, Universidad Autónoma de Guerrero, Carlos E. Adame No. 54 Col. Garita, Acapulco 39650, MexicoWe perform a detailed computational study of the recently introduced Sombor indices on random networks. Specifically, we apply Sombor indices on three models of random networks: Erdös-Rényi networks, random geometric graphs, and bipartite random networks. Within a statistical random matrix theory approach, we show that the average values of Sombor indices, normalized to the order of the network, scale with the average degree. Moreover, we discuss the application of average Sombor indices as complexity measures of random networks and, as a consequence, we show that selected normalized Sombor indices are highly correlated with the Shannon entropy of the eigenvectors of the adjacency matrix.https://www.mdpi.com/1099-4300/23/8/976computational analysis of networksSombor indicesdegree–based topological indicesrandom networks
collection DOAJ
language English
format Article
sources DOAJ
author R. Aguilar-Sánchez
J. A. Méndez-Bermúdez
José M. Rodríguez
José M. Sigarreta
spellingShingle R. Aguilar-Sánchez
J. A. Méndez-Bermúdez
José M. Rodríguez
José M. Sigarreta
Normalized Sombor Indices as Complexity Measures of Random Networks
Entropy
computational analysis of networks
Sombor indices
degree–based topological indices
random networks
author_facet R. Aguilar-Sánchez
J. A. Méndez-Bermúdez
José M. Rodríguez
José M. Sigarreta
author_sort R. Aguilar-Sánchez
title Normalized Sombor Indices as Complexity Measures of Random Networks
title_short Normalized Sombor Indices as Complexity Measures of Random Networks
title_full Normalized Sombor Indices as Complexity Measures of Random Networks
title_fullStr Normalized Sombor Indices as Complexity Measures of Random Networks
title_full_unstemmed Normalized Sombor Indices as Complexity Measures of Random Networks
title_sort normalized sombor indices as complexity measures of random networks
publisher MDPI AG
series Entropy
issn 1099-4300
publishDate 2021-07-01
description We perform a detailed computational study of the recently introduced Sombor indices on random networks. Specifically, we apply Sombor indices on three models of random networks: Erdös-Rényi networks, random geometric graphs, and bipartite random networks. Within a statistical random matrix theory approach, we show that the average values of Sombor indices, normalized to the order of the network, scale with the average degree. Moreover, we discuss the application of average Sombor indices as complexity measures of random networks and, as a consequence, we show that selected normalized Sombor indices are highly correlated with the Shannon entropy of the eigenvectors of the adjacency matrix.
topic computational analysis of networks
Sombor indices
degree–based topological indices
random networks
url https://www.mdpi.com/1099-4300/23/8/976
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