This paper proposes a semi-local centrality measure to identify influential nodes in complex networks. The method balances the simplicity of degree centrality with the effectiveness of more computationally intensive measures like betweenness and closeness centrality. The proposed measure considers both the nearest and next nearest neighbors of a node, making it more effective than degree centrality in identifying influential nodes. The performance of the method is evaluated using the Susceptible–Infected–Recovered (SIR) model, which simulates the spread of information or disease through a network. The results show that the proposed method performs almost as well as closeness centrality, but with much lower computational complexity. The method is tested on four real-world networks: blogs, co-authorship, router-level internet topology, and email communication. The results demonstrate that the proposed method can effectively identify influential nodes in these networks. The method is also compared with other centrality measures, and it is found to be more effective than degree and betweenness centrality measures. The paper also discusses the relationship between different centrality measures and their effectiveness in identifying influential nodes. The results show that the proposed method is more effective than other measures in capturing the influence of nodes in spreading processes. The method is particularly effective in heterogeneous networks where the ranking problem is worth further investigation. The paper concludes that the proposed method is a promising approach for identifying influential nodes in complex networks.This paper proposes a semi-local centrality measure to identify influential nodes in complex networks. The method balances the simplicity of degree centrality with the effectiveness of more computationally intensive measures like betweenness and closeness centrality. The proposed measure considers both the nearest and next nearest neighbors of a node, making it more effective than degree centrality in identifying influential nodes. The performance of the method is evaluated using the Susceptible–Infected–Recovered (SIR) model, which simulates the spread of information or disease through a network. The results show that the proposed method performs almost as well as closeness centrality, but with much lower computational complexity. The method is tested on four real-world networks: blogs, co-authorship, router-level internet topology, and email communication. The results demonstrate that the proposed method can effectively identify influential nodes in these networks. The method is also compared with other centrality measures, and it is found to be more effective than degree and betweenness centrality measures. The paper also discusses the relationship between different centrality measures and their effectiveness in identifying influential nodes. The results show that the proposed method is more effective than other measures in capturing the influence of nodes in spreading processes. The method is particularly effective in heterogeneous networks where the ranking problem is worth further investigation. The paper concludes that the proposed method is a promising approach for identifying influential nodes in complex networks.