  Graph Neural Networks (GNNs) have achieved state-of-the-art results for
semi-supervised node classification on graphs. Nevertheless, the challenge of
how to effectively learn GNNs with very few labels is still under-explored. As
one of the prevalent semi-supervised methods, pseudo-labeling has been proposed
to explicitly address the label scarcity problem. It aims to augment the
training set with pseudo-labeled unlabeled nodes with high confidence so as to
re-train a supervised model in a self-training cycle. However, the existing
pseudo-labeling approaches often suffer from two major drawbacks. First, they
tend to conservatively expand the label set by selecting only high-confidence
unlabeled nodes without assessing their informativeness. Unfortunately, those
high-confidence nodes often convey overlapping information with given labels,
leading to minor improvements for model re-training. Second, these methods
incorporate pseudo-labels to the same loss function with genuine labels,
ignoring their distinct contributions to the classification task. In this
paper, we propose a novel informative pseudo-labeling framework, called
InfoGNN, to facilitate learning of GNNs with extremely few labels. Our key idea
is to pseudo label the most informative nodes that can maximally represent the
local neighborhoods via mutual information maximization. To mitigate the
potential label noise and class-imbalance problem arising from pseudo labeling,
we also carefully devise a generalized cross entropy loss with a class-balanced
regularization to incorporate generated pseudo labels into model re-training.
Extensive experiments on six real-world graph datasets demonstrate that our
proposed approach significantly outperforms state-of-the-art baselines and
strong self-supervised methods on graphs.
