关键词: 链接预测, 合著关系, 异构社会网络, 语义属性, 元路径

Abstract:

In heterogeneous social networks, the prediction of co-authorship is a representative prediction of relationship, which is quite different from the link prediction method of homogeneous networks in node representation, network construction and so on. Fully considering the unique information of meta-path and node attribute characteristics of heterogeneous networks, a composite vectorization representation of nodes is proposed, which combines the TF-IDF features of nodes with network embedding based on Metapath2vec. In the representation of meta-path, the same type of nodes in meta-path are merged and reconstructed to further extract the implicit information between the same type of nodes in meta-path. Also, the prediction of co-authorship in bibliographic network is realized by Convolutional Neural Network（CNN）. Experimental results show that the method of complex vectorization of nodes and reconstruction of meta-path can better characterize heterogeneous social networks, as well as, better evaluation indexes of prediction are given compared with other methods.

Key words: link prediction, co-authorship, heterogeneous social network, semantic attributes, meta-path