Accurately identifying influential nodes within complex networks is crucial for understanding information and influence propagation. Existing state-of-the-art algorithms, while powerful, often rank all nodes, which can be computationally expensive and unnecessary for many applications. In this paper, we propose a simple yet efficient approach that overcomes these limitations. Initially, a systematic sampling methodology was employed to strategically select a subset of nodes from the network, representing a small fraction of its entirety. Subsequently, the betweenness centrality of these sampled nodes was estimated to facilitate their ranking. To assess the performance of our sampling method alongside alternative algorithms, we employ the stochastic Susceptible– Infected–Recovered (SIR) information diffusion model to compute various metrics including the infection scale, the final infected scale over time, and the average distance between spreaders. Our experimental findings, conducted on real-world networks, indicate that our proposed method accurately identifies influential nodes while maintaining significant computational efficiency.