  Graph neural networks (GNNs) have achieved great success in node
classification tasks. However, existing GNNs naturally bias towards the
majority classes with more labelled data and ignore those minority classes with
relatively few labelled ones. The traditional techniques often resort
over-sampling methods, but they may cause overfitting problem. More recently,
some works propose to synthesize additional nodes for minority classes from the
labelled nodes, however, there is no any guarantee if those generated nodes
really stand for the corresponding minority classes. In fact, improperly
synthesized nodes may result in insufficient generalization of the algorithm.
To resolve the problem, in this paper we seek to automatically augment the
minority classes from the massive unlabelled nodes of the graph. Specifically,
we propose \textit{GraphSR}, a novel self-training strategy to augment the
minority classes with significant diversity of unlabelled nodes, which is based
on a Similarity-based selection module and a Reinforcement Learning(RL)
selection module. The first module finds a subset of unlabelled nodes which are
most similar to those labelled minority nodes, and the second one further
determines the representative and reliable nodes from the subset via RL
technique. Furthermore, the RL-based module can adaptively determine the
sampling scale according to current training data. This strategy is general and
can be easily combined with different GNNs models. Our experiments demonstrate
the proposed approach outperforms the state-of-the-art baselines on various
class-imbalanced datasets.
