Abstract
Identifying influential nodes in complex networks is vital for understanding their structure and dynamic behavior. Although methods based on a single characteristic of nodes have been demonstrated to be effective in specific scenarios, the information they provide in dealing with the global aspect of the network is often limited or incomplete. In this paper, we present a measure that combines the degree and the average neighbor degree to evaluate the influence of nodes. As a supplement, we also propose the corresponding gravity index. Experiments on synthetic and real-world networks show that the method proposed in this paper is superior to previous methods based on the single characteristic of nodes in most scenarios. Our gravity strategy is also highly competitive compared with the current famous gravity method.
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 callMore From: IEEE Journal on Emerging and Selected Topics in Circuits and Systems
Disclaimer: 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.
