title: A perturbation-based framework for link prediction via non-negative matrix factorization.
abstract: Many link prediction methods have been developed to infer unobserved links or predict latent links based on the observed network structure. However, due to network noises and irregular links in real network, the performances of existed methods are usually limited. Considering random noises and irregular links, we propose a perturbation-based framework based on Non-negative Matrix Factorization to predict missing links. We first automatically determine the suitable number of latent features, which is inner rank in NMF, by Colibri method. Then, we perturb training set of a network by perturbation sets many times and get a series of perturbed networks. Finally, the common basis matrix and coefficients matrix of these perturbed networks are obtained via NMF and form similarity matrix of the network for link prediction. Experimental results on fifteen real networks show that the proposed framework has competitive performances compared with state-of-the-art link prediction methods. Correlations between the performances of different methods and the statistics of networks show that those methods with good precisions have similar consistence.
authors.affiliation: School of Computer Science and Technology, Tianjin University, Tianjin, 300350, China.
authors.forename: Wenjun
authors.initials: W
authors.lastname: Wang
authors.affiliation: School of Computer Science and Technology, Tianjin University, Tianjin, 300350, China.|School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan, 250101, China.
authors.forename: Fei
authors.initials: F
authors.lastname: Cai
authors.affiliation: School of Computer Science and Technology, Tianjin University, Tianjin, 300350, China.
authors.forename: Pengfei
authors.initials: P
authors.lastname: Jiao
authors.affiliation: School of Marine Science and Technology, Tianjin University, Tianjin, 300072, China.
authors.forename: Lin
authors.initials: L
authors.lastname: Pan