关键词: 链接预测, 半监督学习, 自我训练, 协同训练, 社会网络分析

Abstract: Link prediction is the key issue in social network analysis, which aims to predict new links from the known networks. In real networks, there exist a large number of unlinked node pairs, whose intrinsic information can be helpful to the link prediction task. In this paper, link prediction is looked as a binary classification problem, where the semi-supervised learning techniques are used to take advantage of unlabeled data in learning process. Two semi-supervised paradigms are used：self-training and co-training. The experimental results in real datasets Enron and DBLP show that, exploiting unlabeled data can effectively improve the accuracy of the link prediction.

Key words: link prediction, semi-supervised learning, self-training, co-training, social network analysis