关键词: 多社交网络, 链路预测, 元路径, 矩阵分解

Abstract: It is of great significance to predict links across networks based on information of different users on multi-source social networks, which is helpful for user recommendation, behavior analysis and preference recommendation. The traditional link prediction technologies only consider the local structural characteristics. Some networks??sizes are large, nodes are sparse, and there are a large number of isolated points, which may lead to difficulty in modeling and low computation efficiency. Based on this, this paper proposes a method for extracting meta-path and matrix decomposition to find hidden features using link prediction methods across networks. First, a network graph is contructed based on social relationship among users across social network; then, meta-paths are automatically extracted by using the node active degree and the edge active degree, and the meta-path information related to the target type objects is mapped to the low-dimensional space by using matrix factorization. Finally, the ensemble classification method is used to optimize the link prediction model. The experiments show that the link prediction method has higher accuracy.

Key words: multi-social networks, link prediction, meta-path, matrix factorization