Pairwisely constrained symmetric non-negative matrix factorization for link prediction in complex networks using degree-related clustering coefficient and improved closeness centrality
摘要
The aim of link prediction is to predict missing links or eliminate spurious links and new links in future network. Among different types of link prediction algorithms, non-negative matrix factorization(NMF)-based methods have become a promising and competitive algorithm with the advantages of dimension reduction and higher prediction accuracy. However, existing NMF-based algorithms have some problems as follows: (1) NMF failure directly captures the neighbor capability of the node; (2) most NMF-based methods do not guarantee that the additional information can effectively improve the performance. To dismiss the above limitations, a novel link prediction model named Pairwisely Constrained Symmetric Non-Negative Matrix Factorization via Degree-related clustering coefficient and Improved closeness centrality (PCSNMF