Abstract
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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
