Link Prediction on Evolving Data Using Tensor-based Common Neighbor
Recently there has been increasingly 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 concerns network structure in terms of traditional graph theory, which lack information about evolving network. In this paper we proposed a novel tensorbased prediction method, which is designed through two steps: First, tracking time-dependent network snapshots in adjacency matrices which form a multiway 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 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.
Link prediction Tensor Temporal network analysis
Huayang Cui
The Department Of Computer And Science Harbin Institute Of Technology Weihai, China
国际会议
杭州
英文
921-924
2012-10-28（万方平台首次上网日期，不代表论文的发表时间）