Distance-parameterized h-index gravity model for influential node identification in complex networks

Senbin Yu, Wenjie Wang, Yunheng Wang, Haichen Chen, Xinyi Gan and Peng Zhang

Physica A: Statistical Mechanics and its Applications, 2025, vol. 666, issue C

Abstract: Identifying influential nodes in complex networks through gravity-based models remains challenging due to the complex interplay between node quality and distance metrics. We propose a novel physics-inspired framework that uniquely integrates these parameters by treating inter-node distance as a dynamic factor in determining node quality. This approach extends the traditional h-index to a distance-parameterized neighborhood h-index, where node influence is quantified through iterative distance-based traversal. The framework introduces a saturation distance parameter to optimize the interaction range, addressing a fundamental limitation in existing gravity-based models. We validated our method on eight real-world networks using the Susceptible-Infected-Recovered (SIR) epidemic model. Results demonstrate superior performance in three aspects: higher correlation with SIR spreading outcomes, enhanced monotonicity in influence ranking, and further improved node distinguishability compared to nine conventional methods. This gravitational framework provides a more accurate and computationally efficient approach for identifying critical nodes in epidemic control and information diffusion.

Keywords: Gravity model; Saturation distance; Complex networks; Node influence; h-index (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:666:y:2025:i:c:s0378437125001700

DOI: 10.1016/j.physa.2025.130518

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