  Influential nodes play a critical role in boosting or curbing spreading
phenomena in complex networks. Numerous centrality measures have been proposed
for identifying and ranking the nodes according to their importance. Classical
centrality measures rely on various local or global properties of the nodes.
They do not take into account the network community structure. Recently, a
growing number of researches have shifted to community-aware centrality
measures. Indeed, it is a ubiquitous feature in a vast majority of real-world
networks. In the literature, the focus is on designing community-aware
centrality measures. However, up to now, there is no systematic evaluation of
their effectiveness. This study fills this gap. It allows answering which
community-aware centrality measure should be used in practical situations. We
investigate seven influential community-aware centrality measures in an
epidemic spreading process scenario using the Susceptible-Infected-Recovered
(SIR) model on a set of fifteen real-world networks. Results show that
generally, the correlation between community-aware centrality measures is low.
Furthermore, in a multiple-spreader problem, when resources are available,
targeting distant hubs using Modularity Vitality is more effective. However,
with limited resources, diffusion expands better through bridges, especially in
networks with a medium or strong community structure.
