  Graph neural networks have achieved significant success in representation
learning. However, the performance gains come at a cost; acquiring
comprehensive labeled data for training can be prohibitively expensive. Active
learning mitigates this issue by searching the unexplored data space and
prioritizing the selection of data to maximize model's performance gain. In
this paper, we propose a novel method SMARTQUERY, a framework to learn a graph
neural network with very few labeled nodes using a hybrid uncertainty reduction
function. This is achieved using two key steps: (a) design a multi-stage active
graph learning framework by exploiting diverse explicit graph information and
(b) introduce label propagation to efficiently exploit known labels to assess
the implicit embedding information. Using a comprehensive set of experiments on
three network datasets, we demonstrate the competitive performance of our
method against state-of-the-arts on very few labeled data (up to 5 labeled
nodes per class).
