Abstract
Most of existing methods of link prediction are based on the similarity of network structures and the weight of edges, but they do not effectively use temporal information of forming the weight of edges. The behavior synchronization of two nodes is often caused by the link relationship between them, so the behavior synchronization of nodes has been widely used in many researches of network structure reconstruction to conjecture whether there is a link relationship between any pair of nodes. In this study, we attempt to introduce node synchronization information into the field of link prediction, and propose a novel link prediction algorithm which integrates the synchronization index of node behaviors with network topological similarity. By analyzing and comparing two types of six real-life network data, the proposed method can effectively improve the accuracy of link prediction. Compared with the existing methods, the performance of precision can increase by 15.3% to 68.2%. This study not only finds the joint influence of local structure similarity and behavior synchronization index on link prediction, but also reveals intrinsic structures and dynamic characteristics of different types of real-life weighted 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.
