Modelagem em redes MULTIPLEX: Uma Proposta Baseada na Centralidade do Posicionamento Estrutural a partir da Caracterização das Entidades da Rede

Abstract

Network science emerges as a powerful approach to understanding the diverse interconnected systems of an increasingly interconnected world. Using mathematical, computational, and statistical models, this area allows for the identification of structural patterns, the prediction of behaviors, and the optimization of processes across multiple domains of knowledge. This multidisciplinary combination allows us to explore existing relationships through their metrics. However, the absence of the different characteristics that affect the entities present in the structural relationship can lead to a misinterpretation of the results obtained in relation to the real network. Given this scenario, this thesis aims to propose a metric that aggregates multiple criteria to represent the structural positioning of vertices in the network, through the Critical Points Multiplex Centrality Index (cpmci). This integrates the characteristics affecting the elements present in the entities that compose the network through a multilayer representation, supported by a relevance index (φ) that describes the weights associated with vertices and edges, as well as a factor representing the importance index (β) of the vertex entity that constitutes the network. The application of the metric results in a structural repositioning of the vertices in relation to the root vertex, obtained through a tree structure, with the network defined based on quantitative derived from the established criteria and weights, aiming at decision- making.