Depth-based subgraph convolutional auto-encoder for network representation learning
作者：
Highlights：
• To our knowledge, DS-CAE is the first convolution-based deep learning method for unsupervised network representation learning.Similar to the convolution for image processing, a subgraph-based convolution operation is proposed to scan a tree which is extracted from various graph structures.
• In contrast to most existing models for unsupervised learning on graph-structured data, DS-CAE can capture highly non-linear network structure by simultaneously integrating raw node information and network structure into network representation learning.
• Due to the deep representation, DS-CAE can map graphs to highly nonlinear deeply learned spaces to effectively preserve both the local and global information in original space.
摘要
•To our knowledge, DS-CAE is the first convolution-based deep learning method for unsupervised network representation learning.Similar to the convolution for image processing, a subgraph-based convolution operation is proposed to scan a tree which is extracted from various graph structures.•In contrast to most existing models for unsupervised learning on graph-structured data, DS-CAE can capture highly non-linear network structure by simultaneously integrating raw node information and network structure into network representation learning.•Due to the deep representation, DS-CAE can map graphs to highly nonlinear deeply learned spaces to effectively preserve both the local and global information in original space.
论文关键词：Network representation learning,Graph convolutional neural network,Node classification
论文评审过程：Received 12 September 2018, Revised 29 December 2018, Accepted 13 January 2019, Available online 6 February 2019, Version of Record 11 February 2019.
论文官网地址：https://doi.org/10.1016/j.patcog.2019.01.045
