Abstract
By abstracting the complex structure of social network into undirected graph, nodes into pages and edges into hyperlinks, and combining PageRank algorithm with the discovery of key nodes in social networks, this thesis comes up with a new algorithm of key nodes premised on improved PageRank algorithm, and finally employs microblog data as data set. Through the experiment of comparing KeyRank algorithm with TIPR algorithm, it can be concluded that the PageRank algorithm proposed in this thesis is comparatively suitable for discovering key nodes. Under the same data set, the efficiency of identifying key nodes is raised by 30% in comparison with the other two algorithms.
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.
