Distance entropy cartography characterises centrality in complex networks

TL;DR

This paper introduces distance entropy as a new measure of node homogeneity in complex networks, enhancing centrality analysis and improving predictions of word learning in linguistic networks.

Contribution

The paper presents a novel distance entropy measure and combines it with closeness centrality to create a network cartography that reduces ranking degeneracy.

Findings

Distance entropy provides a new perspective on node homogeneity.

Combining distance entropy with closeness centrality improves network analysis.

Distance entropy better predicts word learning in linguistic networks.

Abstract

We introduce distance entropy as a measure of homogeneity in the distribution of path lengths between a given node and its neighbours in a complex network. Distance entropy defines a new centrality measure whose properties are investigated for a variety of synthetic network models. By coupling distance entropy information with closeness centrality, we introduce a network cartography which allows one to reduce the degeneracy of ranking based on closeness alone. We apply this methodology to the empirical multiplex lexical network encoding the linguistic relationships known to English speaking toddlers. We show that the distance entropy cartography better predicts how children learn words compared to closeness centrality. Our results highlight the importance of distance entropy for gaining insights from distance patterns in complex networks.