Identifying influential nodes in complex networks is of great significance for clearly understanding network structure and maintaining network stability. Researchers have proposed many classical methods to evaluate the propagation impact of nodes, but there is still some room for improvement in the identification accuracy. Degree centrality is widely used because of its simplicity and convenience, but it has certain limitations. We divide the nodes into neighbor layers according to the distance between the surrounding nodes and the measured node. Considering that the node’s neighbor layer information directly affects the identification result, we propose a new node influence identification method by combining degree centrality information about itself and neighbor layer nodes. This method first superimposes the degree centrality of the node itself with neighbor layer nodes to quantify the effect of neighbor nodes, and then takes the nearest neighborhood several times to characterize node influence. In order to evaluate the efficiency of the proposed method, the susceptible–infected–recovered (SIR) model was used to simulate the propagation process of nodes on multiple real networks. These networks are unweighted and undirected networks, and the adjacency matrix of these networks is symmetric. Comparing the calculation results of each method with the results obtained by SIR model, the experimental results show that the proposed method is more effective in determining the node influence than seven other identification methods.

Accurate identification of influential nodes facilitates the control of rumor propagation and interrupts the spread of computer viruses. Many classical approaches have been proposed by researchers regarding different aspects. To explore the impact of location information in depth, this paper proposes an improved global structure model to characterize the influence of nodes. The method considers both the node’s self-information and the role of the location information of neighboring nodes. First, degree centrality of each node is calculated, and then degree value of each node is used to represent self-influence, and degree values of the neighbor layer nodes are divided by the power of the path length, which is path attenuation used to represent global influence. Finally, an extended improved global structure model that considers the nearest neighbor information after combining self-influence and global influence is proposed to identify influential nodes. In this paper, the propagation process of a real network is obtained by simulation with the SIR model, and the effectiveness of the proposed method is verified from two aspects of discrimination and accuracy. The experimental results show that the proposed method is more accurate in identifying influential nodes than other comparative methods with multiple networks.

Accurately identifying influential nodes in a complex network is of great significance to information dissemination. At present, researchers have put forward methods such as Degree centrality, Betweenness centrality, Closeness centrality and Eigenvector centrality, but these methods have certain limitations. There are many factors that affect the results of the node sorting, such as the number of neighbor nodes, node location information, etc. If multiple factors are combined, the proposed method can show more characteristics. First, closeness centrality of neighboring nodes is accumulated to reflect the influence of location information, and then integrated with Eigenvector centrality, the ECCN centrality is proposed to identify the network Influential nodes, this method integrates the path length and the number and quality of neighbor nodes. The SIR propagation model is used to simulate the influence of nodes on multiple real networks, and all centralities are compared with the results of the SIR model. Experimental analysis shows that the proposed method can more accurately identify influential nodes than other methods.

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