Enhancing robustness of link prediction for noisy complex networks

In recent years, complex networks research have attracted considerable attention, in which link prediction has been taken as an effective tool for predicting missing links or identifying spurious links from available datasets. In reality, the collected networks are always noisy and represent patterns of measured structure, not the true structure of complex systems. However, most of the existing link prediction methods have been developed based on the assumption that the observed networks are reliable. Therefore, how to strengthen the robustness of link prediction methods thereby improving prediction accuracy in noisy networks becomes an important problem. In this paper, we propose a novel link prediction method which combines low-rank representation and non-negative matrix factorization for similarity matrix calculation. The low-rank module decomposes the adjacent matrix of networks into a low rank backbone structure and a sparse noise matrix, and the matrix factorization module characterizes the structural patterns of networks comprehensively with multiple perturbation mechanism. The idea behind the method is that we reshape the original network through the addition and deletion of some links identified by low-rank modeling, thus the important parts of the original network are emphasized. Based on the exaggerated but characteristic network, the potential links may be predicted more accurately. Experimental results on synthetic and real-world networks demonstrate that the proposed method performs better than state-of-the-art methods for link prediction in noisy networks.