Node Importance Measurement and Simulation of Complex Network Based on K-index Expansion

With the continuous growth of the number of virtual enterprise alliances and the continuous evolution of alliance forms, it is of practical significance to mining important members of the alliance. Abstract the virtual enterprise alliance into a complex network, in order to distinguish the importance of nodes, based on the centrality of K-index, this paper proposes the LKP centrality method to identify influential nodes in the network, which takes into account the K-index value of the node itself and the K-index contribution of neighbor nodes. Three methods of monotonic function, network efficiency and SIR propagation model verify the effectiveness of the method. The simulation experiment results of 6 real networks show that compared with the K-index, H-index, Closeness centrality and LK-index centrality methods, it is more accurate to evaluate the importance of complex networks.