title: A Noise-Filtering Method for Link Prediction in Complex Networks.
abstract: Link prediction plays an important role in both finding missing links in networked systems and complementing our understanding of the evolution of networks. Much attention from the network science community are paid to figure out how to efficiently predict the missing/future links based on the observed topology. Real-world information always contain noise, which is also the case in an observed network. This problem is rarely considered in existing methods. In this paper, we treat the existence of observed links as known information. By filtering out noises in this information, the underlying regularity of the connection information is retrieved and then used to predict missing or future links. Experiments on various empirical networks show that our method performs noticeably better than baseline algorithms.
authors.affiliation: College of Electrical and Information Engineering, Hunan University, Changsha, Hunan Province, China.
authors.forename: Bo
authors.initials: B
authors.lastname: Ouyang
authors.affiliation: School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou, Zhejiang Province, China.
authors.forename: Lurong
authors.initials: L
authors.lastname: Jiang
authors.affiliation: College of Electrical and Information Engineering, Hunan University, Changsha, Hunan Province, China.
authors.forename: Zhaosheng
authors.initials: Z
authors.lastname: Teng
mesh_terms: D000465:Algorithms; D008962:Models, Theoretical; D059629:Signal-To-Noise Ratio