  Identifying key nodes is crucial for accelerating or impeding dynamic
spreading in a network. Community-aware centrality measures tackle this problem
by exploiting the community structure of a network. Although there is a growing
trend to design new community-aware centrality measures, there is no systematic
investigation of the proposed measures' effectiveness. This study performs an
extensive comparative evaluation of prominent community-aware centrality
measures using the Susceptible-Infected-Recovered (SIR) model on real-world
online social networks. Overall, results show that K-shell with Community and
Community-based Centrality measures are the most accurate in identifying
influential nodes under a single-spreader problem. Additionally, the epidemic
transmission rate doesn't significantly affect the behavior of the
community-aware centrality measures.
