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Unlike classical centrality measures, recently developed community-awarecentrality measures use a network's community structure to identify influentialnodes in complex networks. This paper investigates their relationship on a setof fifty real-world networks originating from various domains. Results showthat classical and community-aware centrality measures generally exhibit low tomedium correlation values. These results are consistent across networks.Transitivity and efficiency are the most influential macroscopic networkfeatures driving the correlation variation between classical andcommunity-aware centrality measures. Additionally, the mixing parameter, themodularity, and the Max-ODF are the main mesoscopic topological propertiesexerting the most substantial effect.

How Correlated are Community-aware and Classical Centrality Measures in Complex Networks?