Identifying Influential Nodes in Complex Networks Based on Node Itself and Neighbor Layer Information

Abstract

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.

Generalization of Relative Change in a Centrality Measure to Identify Vital Nodes in Complex Networks

“…N = {1, 2, • • • , n} and n denotes total number of nodes in a network. We find correlation between the new rankings (new measures) with basic ranking (X = 1, 2, 3, • • • , N ) [38], [57], [58]. Table 5 shows that correlation between new measures and basic centralities for all vertices.…”

Controlling COVID-19 transmission with isolation of influential nodes

“…3 , Fig. 4 , from previous published papers [1] , [34] , [35] , [36] are presented to illustrate the application of centrality measures and proposed method based on relative closeness to the ideal solution. First network containing 12 nodes and 13 edges is presented from reference [34] .…”

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Fig. 2 A network containing 13 nodes and 19 edges [35] . Four top ranking nodes highlighted with red color.

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Improvement of Delegated Proof of Stake Consensus Mechanism Based on Vague Set and Node Impact Factor

“…In measuring the importance of nodes in the system, Zhu used a way to determine the degree centrality of nodes based on node itself and its neighbor layer information [ 25 ]. Similar to this, we propose an easier method to determine the impact factor of different nodes in the network.…”