  Inspired by practical importance of social networks, economic networks,
biological networks and so on, studies on large and complex networks have
attracted a surge of attentions in the recent years. Link prediction is a
fundamental issue to understand the mechanisms by which new links are added to
the networks. We introduce the method of robust principal component analysis
(robust PCA) into link prediction, and estimate the missing entries of the
adjacency matrix. On one hand, our algorithm is based on the sparsity and low
rank property of the matrix, on the other hand, it also performs very well when
the network is dense. This is because a relatively dense real network is also
sparse in comparison to the complete graph. According to extensive experiments
on real networks from disparate fields, when the target network is connected
and sufficiently dense, whatever it is weighted or unweighted, our method is
demonstrated to be very effective and with prediction accuracy being
considerably improved comparing with many state-of-the-art algorithms.
