  Meta-learning is an important approach to improve machine learning
performance with a limited number of observations for target tasks. However,
when observations are unbalancedly obtained, it is difficult to improve the
performance even with meta-learning methods. In this paper, we propose an
active learning method for meta-learning on node response prediction tasks in
attributed graphs, where nodes to observe are selected to improve performance
with as few observed nodes as possible. With the proposed method, we use models
based on graph convolutional neural networks for both predicting node responses
and selecting nodes, by which we can predict responses and select nodes even
for graphs with unseen response variables. The response prediction model is
trained by minimizing the expected test error. The node selection model is
trained by maximizing the expected error reduction with reinforcement learning.
We demonstrate the effectiveness of the proposed method with 11 types of road
congestion prediction tasks.
