Influential nodes identification based on Quasi-Laplacian Gravity Model

Identifying influential nodes, which play a critical role in the structure and dynamics of complex networks, is a fundamental problem in network analysis. While numerous centrality measures have been developed to address this challenge, most existing methods either focus solely on local or global structural information, or lack the flexibility to incorporate additional network features. Moreover, traditional approaches often struggle to adapt to the diverse and complex nature of real-world networks, limiting their effectiveness in accurately capturing node influence. In this study, we propose the Quasi-Laplacian Gravity Model (QLGM), a novel approach that integrates the Quasi-Laplacian matrix with Gravity Model principles to provide a more comprehensive assessment of node influence. The flexibility of QLGM allows it to serve as a general and extensible framework, capable of incorporating diverse network features and existing centrality measures. By integrating these additional sources of information, QLGM not only enhances the performance of traditional and modern centrality methods but also adapts effectively to various network structures. Experimental results on a range of real-world networks demonstrate the effectiveness and adaptability of the QLGM in the identification of influential nodes.