关键词: 链接预测, 机器学习, 动态网络, 社会网络分析, 集成学习, 监督学习

Abstract:

Link prediction is a crucial problem in social network analysis. Most of the traditional link prediction methods find the missing links or predict the future links merely based on astatic structure of a network, ignoring its dynamics. In order to utilize the dynamic information of the network better and achieve a more preferable result, in this paper, a method with consideration of the network evolution is proposed. A machine learning technique is used to model the change of the structural characteristics in networks. A classifier is trained for each structural feature and a final ensemble result is obtained by weighting all the classifiers. The experimental results in three real collaboration networks show that the proposed method outperforms the traditional static link prediction method and a related dynamic link prediction method, which demonstrates that the results of link prediction are much facilitated with the dynamic information of the network. Moreover, the experimental result also shows that different structural characteristics have different abilities to describe the network dynamics.

Key words: link prediction, machine learning, dynamic network, social network analysis, ensemble learning, supervised learning