This paper introduces a component-aware ranking strategy to identify critical nodes in modular networks. It overcomes the limitations of traditional centrality-based methods that fail to account for mesoscopic complex network structuration. The component structure decomposes networks into local (densely connected areas) and global (bridges between these areas) components allowing for the flexible use of different centrality measures in each component to better identify key nodes. We evaluate the method using the Susceptible-Infected-Recovered (SIR) model on LFR synthetic networks that simulate varying levels of community strength, ranging from tightly knit to highly mixed communities. The experimental results demonstrate that the community-aware strategy significantly improves diffusion containment in networks with strong community structures compared to classical centrality ranking methods. Its performance is less pronounced in moderately modular networks, and in weakly modular networks, its benefits are minimal. Additionally, we explore the impact of combining centrality measures (e.g., Betweenness for global and Degree for local components), finding marginal improvements in moderately mixed networks but no substantial advantages in highly mixed ones. Our findings suggest that the component-aware strategy is particularly suited for networks with pronounced community structures, offering a robust tool for information-spreading applications where targeting influential nodes is crucial.