Identifying Vital Nodes in Complex Networks Using K-Shell and Neighborhood Information

Accurate and effective identification of influential nodes in complex networks remains an open and challenging research topic that is crucial for controlling or promoting network propagation, such as epidemic transmission and immunization. In previous studies, the k-shell decomposition method achieves a certain success in identifying influential nodes in complex networks. Because a high number of nodes are awarded the same ranking score throughout the decomposition process, it is coarse-grained. However, because numerous nodes receive the same ranking score throughout the decomposition process, it is coarse-grained. In this study, we developed an improved centrality approach called KICN that takes into account the iteration index of the redesigned k-shell as well as the effect of the target node's local clustering coefficient as well as the degree and clustering coefficient of its second-level neighbors. Combining the modified k-shell iteration index, the effect of the target node's local clustering coefficient and the degree and clustering coefficient of its second-level neighbors, we created the improved centrality approach known as KICN in this study. To assess the KICN method's performance, the susceptible-infected-recovered (SIR) model was used to imitate nodal dynamic process propagation on real-world networks. The susceptible-infected-recovered (SIR) model was used to replicate nodal dynamic process propagation on actual networks in order to assess the effectiveness of the KICN technique.) And empirical analysis revealed that the KICN outperforms several classical centrality methods as evaluated by kendall's correlation and monotonic function.