2018/05/31 by Stuart Oldham, Ben Fulcher, Linden Parkes +4 · 315 citations
Computer Science · Mathematics · Physics and Astronomy · Social Sciences · #Artificial intelligence #Betweenness centrality #Biology #Centrality #Complex Network Analysis Techniques #Complex network #Computer network #Computer science #Consistency (knowledge bases) #Data mining #Evolutionary biology #Katz centrality #Mathematics #Modularity (biology) #Network analysis #Network controllability #Network science #Network theory #Network topology #Node (physics) #Opinion Dynamics and Social Influence #Physics #Social Capital and Networks #Statistics #Topology (electrical circuits) #cs.SI
paper · pdf · doi:10.1371/journal.pone.0220061
published in PLoS ONE 14(7), e0220061 (Public Library of Science) · Main text (25 pages, 8 figures, 1 table), supplementary information (16 pages, 2 tables) and supplementary figures (17 figures)
arxiv created 2018/10/16 · openalex publication_date 2019/07/26 · arxiv updated 2020/07/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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 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.