Mexican University Ranking Based on Maximal Clique

Montes-Orozco, Edwin; Mora-Gutiérrez, Roman Anselmo; Obregón-Quintana, Bibiana; Silva, Sergio Gerardo de los Cobos; García, Eric Alfredo Rincón; Lara-Velázquez, Pedro; Andrade, Miguel Ángel Gutiérrez

doi:10.1007/978-3-030-29973-6_10

Cited by 5 publications

(7 citation statements)

References 50 publications

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Section: B Methodsmentioning

confidence: 99%

“…SA requires 4 control parameters and, in order to obtain a good performance it is important to find the effective parameter settings for the technique. Then, using the Differential Evolution algorithm (DE) [67] ; we obtain the following values (for more information, see [68] ): Initial temperature \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$T_{i} = 1500$ \end{document} . Final temperature \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$T_{f} = 0.001$ \end{document} .…”

Section: Methodsmentioning

confidence: 99%

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Identification of COVID-19 Spreaders Using Multiplex Networks Approach

et al. 2020

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In this work, we present a methodology to identify COVID-19 spreaders using the analysis of the relationship between socio-cultural and economic characteristics with the number of infections and deaths caused by the COVID-19 virus in different countries. For this, we analyze the information of each country using the complex networks approach, specifically by analyzing the spreaders countries based on the separator set in 5-layer multiplex networks. The results show that, we obtain a classification of the countries based on their numerical values in socioeconomics, population, Gross Domestic Product (GDP), health and air connections; where, in the spreader set there are those countries that have high, medium or low values in the different characteristics; however, the aspect that all the countries belonging to the separator set share is a high value in air connections. INDEX TERMS Complex networks, complex systems, COVID-19, multiplex networks, optimization, social networks.

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Section: B Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Identification of COVID-19 Spreaders Using Multiplex Networks Approach

et al. 2020

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“…On the other hand, the MHES networks were modeled using information available in the EXECUM [9] repository of the National Autonomous University of Mexico (UNAM), where some of the main characteristics are number of professors with recognition from the National System of Researchers (SNI) of the Mexican National Council of Science and Technology (CONACYT), professors with Masters or Ph.D., registered students, accredited academic programs, publications in SCOPUS, publications in indexed journals, postgraduate programs incorporated into CONACYT's National Registry of Quality Graduate Programs (PNPC), among others [10]. e idea of modeling 2-and 3-layer multiplex networks is based on the advantage of analyzing the most important nodes based on the information of 2 or 3 years.…”

Section: Approachmentioning

confidence: 99%

“…SA requires 4 control parameters, and in order to obtain a good performance, it is important to find the effective parameter settings for the technique. en, using the differential evolution algorithm (DE) [51]; we obtain the following values (for more information, see [10]):…”

Section: Complexitymentioning

confidence: 99%

Inverse Percolation to Quantify Robustness in Multiplex Networks

et al. 2020

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Inverse percolation is known as the problem of finding the minimum set of nodes whose elimination of their links causes the rupture of the network. Inverse percolation has been widely used in various studies of single-layer networks. However, the use and generalization of multiplex networks have been little considered. In this work, we propose a methodology based on inverse percolation to quantify the robustness of multiplex networks. Specifically, we present a modified version of the mathematical model for the multiplex-vertex separator problem (m-VSP). By solving the m-VSP, we can find nodes that cause the rupture of the mutually connected giant component (MCGC) and the large viable cluster (LVC) when their links are removed from the network. The methodology presented in this work was tested in a set of benchmark networks, and as case study, we present an analysis using a set of multiplex social networks modeled with information about the main characteristics of the best universities in the world and the universities in Mexico. The results show that the methodology presented in this work can work in different models and types of 2- and 3-layer multiplex networks without dividing the entire multiplex network into single-layer as some techniques described in the specific literature. Furthermore, thanks to the fact that the technique does not require the calculation of some structural measure or centrality metric, and it is easy to scale for networks of different sizes.

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“…On the research side, papers in different indexed journals (SCI, Scopus, CONACYT journal quality index) and patents were also considered. Despite the fact that there is a work related to this database in literature [4], it is limited to analyzing a single year. For the present study the available data was used ranging from 2009 to 2017, this range allows to analyze what is the tendency of the Mexican university system, observing the transitions of some universities among different groups over the years.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Ranking Model among Mexican Universities Using Pattern Recognition

et al. 2021

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The evaluation of quality in higher education is today a matter of great importance in most countries because the allocation of resources should be in accordance with the quality of universities. Due to this, there are numerous initiatives to create instruments and evaluation tools that can offer a quality comparison among institutions and countries, the results of these efforts used to be called international rankings. These rankings include some that are “reputational” or subjective, based on opinion polls applied to groups that, which is estimated, can issue authorized views. There are also “objective” rankings, based on performance indicators, which are calculated from a certain set of empirical data; however, on many occasions these indicators are sponsored by universities with the desire to appear among the best universities and emphasize some characteristics more than others, which makes them untrustworthy and very variable between each other. In this sense, we considered the Comparative Study of Mexican Universities (CSMU), a database of statistical information on education and research of Mexican higher education institutions, this database allows users to be responsible for establishing comparisons and relationships that may exist among existing information items, or building indicators based on their own needs and analysis perspectives (Márquez, 2010). This work develops an unsupervised alternative model of ranking among universities using pattern recognition, specifically clustering techniques, which are based on public access data. The results of the CSMU database are obtained by analyzing 60 universities as a first iteration, but to present the final results UNAM is excluded.

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Mexican University Ranking Based on Maximal Clique

Cited by 5 publications

References 50 publications

Identification of COVID-19 Spreaders Using Multiplex Networks Approach

Identification of COVID-19 Spreaders Using Multiplex Networks Approach

Inverse Percolation to Quantify Robustness in Multiplex Networks

A Comparative Ranking Model among Mexican Universities Using Pattern Recognition

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