Link prediction on evolving network using tensor-based node similarity

Recently there has been increasing interest in researching links between objects in complex networks, which can be helpful in many data mining tasks. One of the fundamental researches of links between objects is link prediction. Many link prediction algorithms have been proposed and perform quite well. However, most of those algorithms only concern network structure in terms of traditional graph theory, which lack information about evolving network. In this paper we proposed a novel tensor-based prediction method, which is designed through two steps: First, tracking time-dependent network snapshots in adjacency matrices which form a multi-way tensor by using exponential smoothing method. Second, apply Common Neighbor algorithm to compute the degree of similarity for each nodes. This algorithm is quite different from other tensor-based algorithms, which also are mentioned in this paper. In order to estimate the accuracy of our link prediction algorithm, we employ various popular datasets of social networks and information platforms, such as Facebook and Wikipedia networks. The results show that our link prediction algorithm performances better than another tensor-based algorithms mentioned in this paper.