This paper introduces a new semi-local centrality metric, Local Average Shortest Path with Extended Neighborhood (LASPN), to identify influential nodes in complex networks. LASPN combines the concept of Local Average Shortest Path (LASP) and the Extended Neighborhood Concept (ENC) to efficiently extract subgraphs from large-scale networks. The metric considers both the topological position and semi-local structure of nodes, including the importance of the node itself and its nearest neighbors. The proposed metric is evaluated through numerical simulations on six real-world networks using the Susceptible-Infected-Recovered (SIR) model. Results show that LASPN outperforms classical and state-of-the-art centrality metrics in terms of Kendall’s τ coefficient, achieving a 2.7% improvement over the best available method. The paper also discusses the computational complexity of LASPN and its effectiveness in handling large-scale networks.This paper introduces a new semi-local centrality metric, Local Average Shortest Path with Extended Neighborhood (LASPN), to identify influential nodes in complex networks. LASPN combines the concept of Local Average Shortest Path (LASP) and the Extended Neighborhood Concept (ENC) to efficiently extract subgraphs from large-scale networks. The metric considers both the topological position and semi-local structure of nodes, including the importance of the node itself and its nearest neighbors. The proposed metric is evaluated through numerical simulations on six real-world networks using the Susceptible-Infected-Recovered (SIR) model. Results show that LASPN outperforms classical and state-of-the-art centrality metrics in terms of Kendall’s τ coefficient, achieving a 2.7% improvement over the best available method. The paper also discusses the computational complexity of LASPN and its effectiveness in handling large-scale networks.