Before revision:  Mostly existing work focus on designing novel algorithm structure to improve the performance, but ignore one common training problem, i.e., could these methods achieve the same performance with limited labelled data? To tackle this research gap , we propose a sampling-based training framework for semi-supervised graph embedding methods to achieve better performance with smaller training data set.
After revision:  Mostly existing work focus on designing novel GCN architectures for better performance, few of them studied a practical yet challenging problem: How to learn GCNs from data with extremely limited annotation? In this paper , we propose a sampling-based training framework for semi-supervised graph embedding methods to achieve better performance with smaller training data set.