General link prediction with influential node identification

Abstract

Link Prediction, which aims to infer the missing or future connections between two nodes, is a key step in many complex network analysis areas such as social friend recommendation and protein function prediction. A majority of existing efforts are devoted to define the influence of neighbor nodes. However, even though recent studies show that node attributes have an added value to network structure for accurate link prediction, it still remains ignoring the real node influence. To address this problem, in this paper we investigate influential node identification technique to formulate a node ranking-based link prediction metric. The general idea of our approach is to exploit the ranking score as the contribution of a common neighbor. Such fundamental mechanism preserve both local structure and global information. Experimental results on real-world networks with two scenario demonstrate that our proposed metrics achieves better performance than existing state-of-the-art local and global similarity methods.