  Graph neural networks (GNNs) have demonstrated significant success in various
applications, such as node classification, link prediction, and graph
classification. Active learning for GNNs aims to query the valuable samples
from the unlabeled data for annotation to maximize the GNNs' performance at a
lower cost. However, most existing algorithms for reinforced active learning in
GNNs may lead to a highly imbalanced class distribution, especially in highly
skewed class scenarios. GNNs trained with class-imbalanced labeled data are
susceptible to bias toward majority classes, and the lower performance of
minority classes may lead to a decline in overall performance. To tackle this
issue, we propose a novel class-balanced and reinforced active learning
framework for GNNs, namely, GCBR. It learns an optimal policy to acquire
class-balanced and informative nodes for annotation, maximizing the performance
of GNNs trained with selected labeled nodes. GCBR designs class-balance-aware
states, as well as a reward function that achieves trade-off between model
performance and class balance. The reinforcement learning algorithm Advantage
Actor-Critic (A2C) is employed to learn an optimal policy stably and
efficiently. We further upgrade GCBR to GCBR++ by introducing a punishment
mechanism to obtain a more class-balanced labeled set. Extensive experiments on
multiple datasets demonstrate the effectiveness of the proposed approaches,
achieving superior performance over state-of-the-art baselines.
