Methodology to quantify robustness in networks: case study—Higher Education System in Mexico

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/s00607-021-00909-x

Cited by 3 publications

(2 citation statements)

References 36 publications

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“…In the work of Montes-Orozco et al [21], the authors present a methodology based on inverse percolation that finds the set of nodes that disconnect the network in order to quantify the robustness in single-layer networks.…”

Section: Related Workmentioning

confidence: 99%

“…On the other hand, the methodology presented in this work is defined as an adaptation of the methodology denoted in [21], which is based on the resolution of the vertex separator problem (VSP) [45]. Specifically, an m-VSP consists in finding a minimum set of nodes C that, being deleted from the network, it produces a disconnection of the MCGC and the LVC of the multiplex network into two connected components (A, B), in such a way that A and B are maximized.…”

Section: Inverse Percolation Through the Adaptation Of Vertexmentioning

confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%

Section: Inverse Percolation Through the Adaptation Of Vertexmentioning

confidence: 99%

Inverse Percolation to Quantify Robustness in Multiplex Networks

et al. 2020

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On the analysis of collaboration networks between industry and academia: the Mexican case of the innovation incentive program

Montes-Orozco,

Miranda,

García-Nájera

et al. 2024

Scientometrics

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HSMVS: heuristic search for minimum vertex separator on massive graphs

Luo,

Guo

2024

PeerJ Computer Science

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In graph theory, the problem of finding minimum vertex separator (MVS) is a classic NP-hard problem, and it plays a key role in a number of important applications in practice. The real-world massive graphs are of very large size, which calls for effective approximate methods, especially heuristic search algorithms. In this article, we present a simple yet effective heuristic search algorithm dubbed HSMVS for solving MVS on real-world massive graphs. Our HSMVS algorithm is developed on the basis of an efficient construction procedure and a simple yet effective vertex-selection heuristic. Experimental results on a large number of real-world massive graphs present that HSMVS is able to find much smaller vertex separators than three effective heuristic search algorithms, indicating the effectiveness of HSMVS. Further empirical analyses confirm the effectiveness of the underlying components in our proposed algorithm.

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Methodology to quantify robustness in networks: case study—Higher Education System in Mexico

Cited by 3 publications

References 36 publications

Inverse Percolation to Quantify Robustness in Multiplex Networks

Inverse Percolation to Quantify Robustness in Multiplex Networks

On the analysis of collaboration networks between industry and academia: the Mexican case of the innovation incentive program

HSMVS: heuristic search for minimum vertex separator on massive graphs

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