Informative Pseudo-Labeling for Graph Neural Networks with Few Labels. (arXiv:2201.07951v1 [cs.LG])

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 …

arxiv graph graph neural networks labeling networks neural networks