A Complex Network Important Node Identification Based on the KPDN Method

Abstract

In complex networks, identifying influential nodes is of great significance for their wide application. The proposed method integrates the correlation properties of local and global, and in terms of global features, the K-shell decomposition method of fusion degree is used to improve the actual discrimination degree of each node. In terms of local characteristics, the Solton index is introduced to effectively show the association relationship between each node and adjacent nodes. Through the analysis and comparison of multiple existing methods, it is found that the proposed method can identify key nodes more accurately so as to help quickly disintegrate the network. The final manual network verification also shows that this method is also suitable for the identification of important nodes of small-world networks and community networks.