A Group-Based Centrality for Undirected Multiplex Networks: A Case Study of the Brazilian Car Wash Operation

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 Group-based Centrality for Undirected Multiplex Networks, to find the most relevant nodes in an undirected multiplex network. As a case study, we use a Brazilian corruption investigation known as the Car Wash Operation. Our proposed centrality outperforms well-known centrality methods such as betweenness, eigenvector, weighted degree, Multiplex PageRank, closeness, and cross-layer degree centrality.