Key node identification and the study of its impact on disease transmission dynamics

Key nodes in complex networks, though few, significantly impact the network, playing a crucial role in the spread of epidemics. This paper aims to propose a more effective method for identifying key nodes in disease transmission dynamics. We first introduce six widely considered indicators including degree centrality, betweenness centrality, h-index, k-shell, cycle ratio and vertex entanglement, and then apply the Analytic Hierarchy Process for each indicator to assign a weight, thereby generating a comprehensive strategy for identifying key nodes. Furthermore, we choose key nodes, which are selected by ranking node importance under different strategies, as both initial infection sources and targets for immunization in a targeted control strategy, observing the effects on disease transmission. The compartment models including SIS and SIR models are employed to simulate disease spread within three network models including Watts–Strogatz small-world, Barabási–Albert scale-free and modular networks. Numerical simulation results show that the comprehensive strategy can more effectively identify the key nodes that significantly influence the peak transmission value and halt disease spread, compared to other general strategies. This study contributes to the broader understanding of epidemic dynamics and offers novel insights for the development of effective infectious disease prevention strategies.