Semi-supervised network embedding with text information

Maoguo Gong, Chuanyu Yao, Yu Xie, Mingliang Xu

Abstract: Highlights•A semi-supervised method based on stacked auto-encoders for network embedding is presented.•We explore the global structural information of the network by the structure preserving module and exploit the text features of nodes by the text representation module.•A label indicator matrix and a supervised loss are proposed for the purpose of determining whether two nodes are in the same class and ensuring that the nodes in the same class have similar embedding vectors.