Bi-objective model for community detection in weighted complex networks :  Non overlap multiobjective k_core  community detection

Gilberto Sinuhe Torres-Cockrell; Roman Anselmo Mora-Gutiérrez; Eric Alfredo Rincón-García; Edwin Montes-Orozco; Sergio Gerardo de-los-Cobos-Silva; Pedro Lara-Velazquez; Miguel Ángel Gutiérrez-Andrade

doi:10.4108/eetinis.v11i4.4059

Authors

Gilberto Sinuhe Torres-Cockrell Universidad Autónoma Metropolitana https://orcid.org/0000-0003-4360-7286
Roman Anselmo Mora-Gutiérrez Universidad Autónoma Metropolitana https://orcid.org/0000-0002-2112-7049
Eric Alfredo Rincón-García Universidad Autónoma Metropolitana https://orcid.org/0000-0001-7837-7818
Edwin Montes-Orozco Universidad Autónoma Metropolitana https://orcid.org/0000-0001-8594-7006
Sergio Gerardo de-los-Cobos-Silva Universidad Autónoma Metropolitana https://orcid.org/0000-0003-1262-6310
Pedro Lara-Velazquez Universidad Autónoma Metropolitana https://orcid.org/0000-0002-8596-8234
Miguel Ángel Gutiérrez-Andrade Universidad Autónoma Metropolitana https://orcid.org/0000-0002-8633-8592

DOI:

https://doi.org/10.4108/eetinis.v11i4.4059

Keywords:

complex networks, k_core, multi-objective, NSGAII, MOSA

Abstract

In this study, we introduce an innovative approach that utilizes complex networks and the k_core method to address community detection in weighted networks. Our proposed bi-objective model aims to simultaneously discover non-overlapping communities while ensuring that the degree of similarity remains below a critical threshold to prevent network degradation. We leverage the k_core structure to detect tightly interconnected node groups, a concept particularly valuable in edge-weighted networks where different edge weights indicate the strength or importance of node relationships. Beyond maximizing the count of k_core communities, our model seeks a homogeneous weight distribution across edges within these communities, promoting stronger cohesion. To tackle this challenge, we implement two multi-target algorithms: Non-dominated Sorting Genetic Algorithm II (NSGAII) and a Multi-Objective Simulated Annealing (MOSA) algorithm. Both algorithms efficiently identify non-overlapping communities with a specified degree 'k'. The results of our experiments reveal a trade-off between maximizing the number of k_core communities and enhancing the homogeneity of these communities in terms of their minimum weighted interconnections. Notably, the MOSA algorithm outperforms NSGAII in both small and large instances, demonstrating its effectiveness in achieving this balance. This approach sheds light on effective strategies for resolving conflicting goals in community detection within weighted networks.

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Author Biographies

Gilberto Sinuhe Torres-Cockrell, Universidad Autónoma Metropolitana

Student

Posgrado en Ciencias y Tecnologías de la Información. Universidad Autónoma Metropolitana Iztapalapa

Roman Anselmo Mora-Gutiérrez , Universidad Autónoma Metropolitana

Profesor Investigador

Departamento de Sistemas. Universidad Autónoma Metropolitana Azcapotzalco.

Eric Alfredo Rincón-García, Universidad Autónoma Metropolitana

Profesor Investigador Titular

Departamento de Ingeniería Eléctrica. Universidad Autónoma Metropolitana Iztapalapa.

Edwin Montes-Orozco , Universidad Autónoma Metropolitana

Profesor Investigador de Tiempo Completo

Departamento de Matemáticas Aplicadas y Sistemas. Universidad Autónoma Metropolitana Cuajimalpa.

Sergio Gerardo de-los-Cobos-Silva , Universidad Autónoma Metropolitana

Profesor Titular C T.C.

Departamento de Ingeniería Eléctrica. Universidad Autónoma Metropolitana Iztapalapa.

Pedro Lara-Velazquez , Universidad Autónoma Metropolitana

Profesor Investigador Titular

Departamento de Ingeniería Eléctrica. Universidad Autónoma Metropolitana Iztapalapa.

Miguel Ángel Gutiérrez-Andrade , Universidad Autónoma Metropolitana

Profesor Investigador Titular

Departamento de Ingeniería Eléctrica. Universidad Autónoma Metropolitana Iztapalapa.

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