Link Prediction via Matrix Completion

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

Searching for Weak Singlet Charged Scalar at the Large Hadron Collider

Weak singlet charged scalar exists in many new physics models beyond the Standard Model. In this work we show that a light singlet charged scalar with mass above 65~GeV is still allowed by the LEP and LHC data. The interactions of the singlet charged scalar with the Standard Model particles are described by operators up to dimension-5. Dominant decay modes of the singlet charged scalar are obtained, and a subtlety involving field redefinition and gauge fixing due to a dimension-5 operator is also clarified. We demonstrate that it is promising to observe the singlet charged scalar at the LHC.Comment: 10 pages, 5 figures; accepted version of PR