Performances and Correlations of Centrality Measures in Complex Networks
Yilin Bi, Xinshan Jiao, Tao Zhou

TL;DR
This study empirically compares 16 centrality measures across 80 real-world networks, revealing their correlations, unique properties, and effectiveness in identifying influential nodes and node sets, with implications for network analysis.
Contribution
It provides a comprehensive empirical analysis of centrality measures, identifying communities of similar measures, their correlation patterns, and their relative performance in influence detection tasks.
Findings
High correlation within measure communities
Five measures capture unique node importance properties
Measures with larger topological distances perform better in node set influence detection
Abstract
Numerous centrality measures have been proposed to evaluate the importance of nodes in networks, yet comparative analyses of these measures remain limited. Based on 80 real-world networks, we conducted an empirical analysis of 16 representative centrality measures. In general, there exists a moderate to high level of correlation between node rankings derived from different measures. We identified two distinct communities: one comprising 4 measures and the other 7 measures. Measures within the same community exhibit exceptionally strong pairwise correlations. In contrast, the remaining five measures display markedly different behaviors, showing weak correlations not only among themselves but also with the other measures. This suggests that each of these five measures likely captures unique properties of node importance. Further analysis reveals that the distribution patterns of the most…
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