  Unlike classical centrality measures, recently developed community-aware
centrality measures use a network's community structure to identify influential
nodes in complex networks. This paper investigates their relationship on a set
of fifty real-world networks originating from various domains. Results show
that classical and community-aware centrality measures generally exhibit low to
medium correlation values. These results are consistent across networks.
Transitivity and efficiency are the most influential macroscopic network
features driving the correlation variation between classical and
community-aware centrality measures. Additionally, the mixing parameter, the
modularity, and the Max-ODF are the main mesoscopic topological properties
exerting the most substantial effect.
