Centrality measures are widely used to rank influential nodes in complex networks. However, traditional ranking schemes often concentrate top-ranked nodes within the same region, limiting their effectiveness in network-wide influence. To address this limitation, we propose a community-aware ranking scheme that distributes influential nodes across different communities, ensuring broader coverage. We evaluate the proposed scheme using the Susceptible-Infected-Recovered (SIR) model on both synthetic and real-world networks across six centrality measures. Our results demonstrate that the community-aware ranking consistently achieves a larger outbreak size compared to the classical descending order scheme, making it more effective for applications such as viral marketing, misinformation control, and vaccination strategies. This approach is adaptable to various network types and centrality measures, offering a robust tool for enhancing diffusion processes.