Abstract
The influence of traditionally composing node degree ignores the fact that effective transmission determines the similarity between endpoints in link prediction. Due to effective transmission, the degrees connected to the common neighbor and the short-path can be regarded as efficient influence, whereas the degrees connected to the noncommon neighbor and the long-path represent redundant influence because of their inefficiency, not to mention the paths disconnected to the target node. Therefore, in this paper, we propose a new method based on punishing the redundant influence (PRI) to improve the accuracy of the link prediction by emphasizing the effective influence and increasing the similarity between node pairs. From the results of extensive experiments in twelve real-world networks, PRI achieves superior performance compared to the traditional methods.
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.
