Abstract
Identifying key nodes in complex networks holds significant application value in fields such as information dissemination and disease spread. The traditional K-shell decomposition method has low time complexity and is suitable for large-scale complex networks; however, it only considers global positional information, leading to lower discrimination. To improve the K-shell decomposition method, many approaches have been proposed by researchers. However, there no algorithm has yet that simultaneously uses the iteration factor and degree to further distinguish nodes with the same K-shell value. To address this issue, we propose a node influence ranking algorithm that integrates K-shell iteration, node degree, and neighbor information, considering both global network position and local topology. Through simulation experiments on eight networks, it was verified that this method provides more accurate ranking results compared to dc, bc, cc, k-shell, Ks + , KSIF, LGI and DCK methods on eight networks, with an average accuracy improvement of 5.15% over the second-best algorithm. In identifying the top 10 key nodes, the KTD algorithm demonstrates higher accuracy than other methods. Additionally, it shows high discriminative power and good time performance, making it suitable for large-scale complex networks.