  Few-shot node classification poses a significant challenge for Graph Neural
Networks (GNNs) due to insufficient supervision and potential distribution
shifts between labeled and unlabeled nodes. Self-training has emerged as a
widely popular framework to leverage the abundance of unlabeled data, which
expands the training set by assigning pseudo-labels to selected unlabeled
nodes. Efforts have been made to develop various selection strategies based on
confidence, information gain, etc. However, none of these methods takes into
account the distribution shift between the training and testing node sets. The
pseudo-labeling step may amplify this shift and even introduce new ones,
hindering the effectiveness of self-training. Therefore, in this work, we
explore the potential of explicitly bridging the distribution shift between the
expanded training set and test set during self-training. To this end, we
propose a novel Distribution-Consistent Graph Self-Training (DC-GST) framework
to identify pseudo-labeled nodes that are both informative and capable of
redeeming the distribution discrepancy and formulate it as a differentiable
optimization task. A distribution-shift-aware edge predictor is further adopted
to augment the graph and increase the model's generalizability in assigning
pseudo labels. We evaluate our proposed method on four publicly available
benchmark datasets and extensive experiments demonstrate that our framework
consistently outperforms state-of-the-art baselines.
