Detecting global bridges in networks

TL;DR

This paper introduces a new measure called bridgeness centrality, which effectively identifies global bridges in networks by decomposing betweenness centrality into local and global components, improving understanding of network structure.

Contribution

It proposes a novel decomposition of betweenness centrality into local and global terms, with a focus on accurately detecting global bridges in networks.

Findings

Bridgeness centrality effectively identifies global bridges.

Algorithmic implementation is efficient for large networks.

Application to real-world networks demonstrates practical utility.

Abstract

The identification of nodes occupying important positions in a network structure is crucial for the understanding of the associated real-world system. Usually, betweenness centrality is used to evaluate a node capacity to connect different graph regions. However, we argue here that this measure is not adapted for that task, as it gives equal weight to "local" centers (i.e. nodes of high degree central to a single region) and to "global" bridges, which connect different communities. This distinction is important as the roles of such nodes are different in terms of the local and global organisation of the network structure. In this paper we propose a decomposition of betweenness centrality into two terms, one highlighting the local contributions and the other the global ones. We call the latter bridgeness centrality and show that it is capable to specifically spot out global bridges. In addition, we introduce an effective algorithmic implementation of this measure and demonstrate its capability to identify global bridges in air transportation and scientific collaboration networks.