Comparative analysis of local variant resistance distance-based gravity model for the identification of influential nodes

The identification of influential nodes in complex networks is a fundamental problem in network science with significant implications for infrastructure resilience, epidemic control, biological discovery and marketing strategies. Traditional centrality measures provide valuable insights but are limited by their reliance on global structures or shortest-path distances. Gravity-based models have been proposed to overcome these gaps by integrating node properties with distance-based interactions, yet most existing formulations are global in scope and computationally demanding. In this study, we propose a novel local resistance distance based gravity model (LRGM) for the identification of influential nodes in complex networks. Unlike global approaches, LRGM restricts interactions to nodes within a truncated resistance distance radius, thereby capturing localized influence while reducing computational complexity. Experimental evaluations demonstrate that the efficiency of LRGM is better than the global centrality measures.