Uma nova centralidade para Redes Multiplex não direcionadas

Abstract

One challenging issue in information science, biological systems, and many other fields is determining the most central or relevant networked systems agents. These networks usually describe scenarios using nodes (objects) and edges (the objects’ relations). The so-called standard centrality measures aim to solve this kind of challenge, ranking the nodes by their supposed relevance and elect the most relevant nodes. This problem becomes more challenging when one single network is not enough to depict the whole scenario. In these cases, we can work with multiplex networks characterized by a set of network layers, each describing interrelationships that can change depending on external factors, e.g., time. This paper proposes a new centrality measure, the Groupbased Centrality for Undirected Multiplex Networks, to find the most relevant nodes in an undirected multiplex network. We use three case study, to describe the centrality usage: a Brazilian corruption investigation known as the Car Wash Operation, the set of books of the Harry Potter franchise, and the Brazilian corruption investigation of public tenders known as the Ghost Bidder Operation. In these tree analyses, our proposed centrality outperforms well-known centrality methods such as betweenness, eigenvector, PageRank, closeness, weighted degree, and multylayer centralities like Multiplex PageRank cross-layer degree centrality.