Published In

PLOS One

Document Type

Article

Publication Date

9-25-2025

Subjects

Real-world networks

Abstract

Numerous centrality measures exist to quantify the influence of edges within a network, with edge betweenness being one of the more well-known measures. However, such measures are inadequate in network percolation scenarios (e.g., the transmission of a disease over a transportation network of highways) as they fail to consider the changing percolation states of edges over time. This paper addresses this limitation by extending percolation centrality, a measure originally developed to evaluate the influence of vertices during a percolation process (i.e., a dynamic spread of a contagion) in the network, to the edge level. The proposed measure, edge percolation centrality, captures both the topological connectivity of the network as well as the percolation states of the edges. Although the algorithm’s observed complexity of O(|V|3.57) makes it computationally intensive, the utility of the proposed edge measure is evident in its application to both synthetic and real-world networks undergoing percolation processes.

Rights

Copyright: © 2025 Durón et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

DOI

10.1371/journal.pone.0331475

Persistent Identifier

https://archives.pdx.edu/ds/psu/44152

Publisher

Public Library of Science (PLoS)

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