  Graph convolution network (GCN) attracts intensive research interest with
broad applications. While existing work mainly focused 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 new learning method by sampling
strategy and model compression to overcome this challenge. Our approach has
multifold advantages: 1) the adaptive sampling strategy largely suppresses the
GCN training deviation over uniform sampling; 2) compressed GCN-based methods
with a smaller scale of parameters need fewer labeled data to train; 3) the
smaller scale of training data is beneficial to reduce the human resource cost
to label them. We choose six popular GCN baselines and conduct extensive
experiments on three real-world datasets. The results show that by applying our
method, all GCN baselines cut down the annotation requirement by as much as
90$\%$ and compress the scale of parameters more than 6$\times$ without
sacrificing their strong performance. It verifies that the training method
could extend the existing semi-supervised GCN-based methods to the scenarios
with the extremely small scale of labeled data.
