Influential Node Identification Method Based on Multi-Order Neighbors and Exclusive Neighborhood

In a complex network, identifying the influence of nodes and locating the key nodes are helpful for analyzing the network structure and determining the position of nodes to control information transmission, redistribute resources, and regulate the network. We propose an influential node identification method based on multi-order neighbors and exclusive neighborhood (MNEN) based on an analysis and investigation of the existing methods for identifying influential nodes. In the MNEN method, a node's influence is determined by two factors: the node itself, its neighboring nodes, and its exclusive neighborhood. The influence of the node itself is calculated based on its degree value and Ks value (the Ks value is obtained using the K-shell method), while the influence contribution of the neighbor node is calculated based on its degree value, Ks value, and the contribution provided by its exclusive neighbor node. To analyze the performance of the algorithm, we use the SIR model as the evaluation basis, conduct simulation experiments to validate the MNEN method, and compare the experimental results to those of other influential node identification methods. The analysis results indicate that the algorithm accurately identifies influential nodes in networks of varying scales, has a positive overall effect, and is somewhat universal.