Consistency and differences between centrality measures across distinct classes of networks

TL;DR

This study analyzes how different centrality measures relate across various real-world networks, revealing that network topology influences these relationships and that clustering based on centrality profiles can identify distinct node roles.

Contribution

It provides a comprehensive comparison of 17 centrality measures across diverse networks and shows how network topology affects their correlations and the classification of node roles.

Findings

Centrality measures are generally positively correlated.

Correlation strength varies with network density and topology.

Clustering based on centrality profiles identifies distinct node roles.

Abstract

The roles of different nodes within a network are often understood through centrality analysis, which aims to quantify the capacity of a node to influence, or be influenced by, other nodes via its connection topology. Many different centrality measures have been proposed, but the degree to which they offer unique information, and such whether it is advantageous to use multiple centrality measures to define node roles, is unclear. Here we calculate correlations between 17 different centrality measures across 212 diverse real-world networks, examine how these correlations relate to variations in network density and global topology, and investigate whether nodes can be clustered into distinct classes according to their centrality profiles. We find that centrality measures are generally positively correlated to each other, the strength of these correlations varies across networks, and network modularity plays a key role in driving these cross-network variations. Data-driven clustering of nodes based on centrality profiles can distinguish different roles, including topological cores of highly central nodes and peripheries of less central nodes. Our findings illustrate how network topology shapes the pattern of correlations between centrality measures and demonstrate how a comparative approach to network centrality can inform the interpretation of nodal roles in complex networks.