Abstract
The study of link prediction in graph theory has received more and more attention in recent years. Considering the attributes of nodes in online social networks are generally inaccurate, it is very important and efficient to use the network structure characteristics rather than nodes’ information to predict edges in networks. In this paper, we present a simple but effective similarity-based prediction strategy based on label propagation, which mimics the communication between people naturally. We perform an experimental comparison of the proposed method against four classic local similarity-based link prediction algorithms using real-world networks. The experimental results show that our method offers higher precision than these well-known approaches. Hence, we can provide more accurate friend recommendations for online social networks and reduce experimental costs in the fields of biology, and better understand the evolution mechanism of complex networks.
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.
