Abstract
With the rapid expansion of internet, the complex networks has become high-dimensional, sparse and redundant. Besides, the problem of link prediction in such networks has also obatined increasingly attention from different types of domains like information science, anthropology, sociology and computer sciences. It makes requirements for effective link prediction techniques to extract the most essential and relevant information for online users in internet. Therefore, this paper attempts to put forward a link prediction algorithm based on non-negative matrix factorization. In the algorithm, we reconstruct the correlation between different types of matrix through the projection of high-dimensional vector space to a low-dimensional one, and then use the similarity between the column vectors of the weight matrix as the scoring matrix. The experiment results demonstrate that the algorithm not only reduces data storage space but also effectively makes the improvements of the prediction performance during the process of sustaining a low time complexity.
Highlights
With the unprecedentedly rapid development of technology, the world has become increasingly complicated with frequent networking
Based on the iterative method for NMF computing, we present an algorithm named NMF-LP for link prediction based on non-negative matrix factorization
We testified the reliability of our algorithm NMF-LP on six benchmark data sets which served networks without node attributes: US airport network(USAir), US political blogs (PB) network, coauthor-ships network between scientists(NS), protein-protein interaction network (PPI), electrical resource grid of the western US(Grid) and Internet(INT)
Summary
With the unprecedentedly rapid development of technology, the world has become increasingly complicated with frequent networking. A number of information, biological, and social systems, ranging from interpersonal relationships to the colony structure, from transportation to the online world, from the ecosystem to the nervous system, can be considered as a network, in which vertices stand for the interactions between vertices or links and entities denote relations. Based on the known network information, we are required to forecast the potential and missing links. This is the objectivity of the forecast challenge linked with the network, due to the dynamic development of the links of complicated network [1, 2].
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