A Topological Centrality Measure for Directed Networks
From MaRDI portal
Publication:6389646
arXiv2201.12907MaRDI QIDQ6389646
Author name not available (Why is that?)
Publication date: 30 January 2022
Abstract: Given a directed network , we are interested in studying the qualitative features of which govern how perturbations propagate across . Various classical centrality measures have been already developed and proven useful to capture qualitative features and behaviors for undirected networks. In this paper, we use topological data analysis (TDA) to adapt measures of centrality to capture both directedness and non-local propagating behaviors in networks. We introduce a new metric for computing centrality in directed weighted networks, namely the quasi-centrality measure. We compute these metrics on trade networks to illustrate that our measure successfully captures propagating effects in the network and can also be used to identify sources of shocks that can disrupt the topology of directed networks. Moreover, we introduce a method that gives a hierarchical representation of the topological influences of nodes in a directed network.
Has companion code repository: https://github.com/lindahe8989/a-topological-centrality-measure-for-directed-networks
No records found.
This page was built for publication: A Topological Centrality Measure for Directed Networks
Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6389646)
