A centrality model for directed graphs based on the Two-Way-Random Path and associated indices for characterizing the nodes

Centrality metrics are one of the most meaningful features in a large number of real-world network systems. In that sense, the Betweenness centrality is a widely used measurement that quantifies the importance of a node in the information flow in a network. Moreover, there is a centrality measure, based on random-paths betweenness centrality, that provides a classification of the nodes of undirected networks, that are able to reinforce dense communities according to their role. In this paper, a new centrality model, based on random-paths betweenness centrality and applied on directed networks, is presented. This model, based on four indices, describes the behaviour of the nodes within the network in terms of its role, such as a transition node, in the same cluster or between clusters. Finally, we evaluate the model with several use cases based on real networks, two of them are proposed and created in this paper, giving insight into some interesting findings about the networks’ features.Financial support for this research has been provided under grant PID2020-112827GB-I00 funded by MCIN/AEI/10.13039/501100011033