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Evaluating global measures of network centralization: Axiomatic and numerical assessments
1Headache and Orofacial Pain Effort (H.O.P.E.), Biologic and Materials Science and Prosthodontics, University of Michigan School of Dentistry, Ann Arbor, Michigan, United States of America.
Plos One
|February 5, 2026
Summary
This study evaluates network centralization measures, finding that normalized betweenness, closeness, and degree centralization are most reliable. These metrics offer complementary insights into network structure and hub importance across diverse real-world networks.
Area of Science:
- Network Science
- Graph Theory
- Computational Social Science
Background:
- Network centralization, driven by hub nodes, significantly influences network dynamics like communication, diffusion, and synchronization.
- Existing centralization measures lack consistent theoretical grounding and empirical reliability across various network types.
Purpose of the Study:
- To systematically assess and compare 11 normalized network centralization measures.
- To identify theoretically sound and empirically reliable measures for quantifying network centralization.
Main Methods:
- Axiomatic framework evaluating measures against six centralization postulates.
- Numerical simulations assessing normalized measures on random graphs.
- Application of selected measures to diverse real-world networks.
Main Results:
- Significant differences were observed among the 11 measures.
- Normalized betweenness, closeness, and degree centralization emerged as the most suitable.
- These three measures revealed distinct aspects of network organization related to hubs: path dominance, accessibility, and degree concentration.
Conclusions:
- A dual evaluation framework clarifies conceptual differences and guides the selection of reliable network centralization metrics.
- Joint application of normalized betweenness, closeness, and degree centralization provides a comprehensive understanding of network structure.
- The findings offer practical guidance for researchers analyzing network centralization across various domains.
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