Semi-supervised network embedding with text information
作者：
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.
摘要
•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.
论文关键词：Network embedding,Structure preserving,Text representation,Stacked auto-encoders
论文评审过程：Received 19 October 2018, Revised 14 March 2020, Accepted 25 March 2020, Available online 28 March 2020, Version of Record 15 April 2020.
论文官网地址：https://doi.org/10.1016/j.patcog.2020.107347
