Graph Convolutional Networks (GCNs) have recently attracted vast interest and
achieved state-of-the-art performance on graphs, but its success could
typically hinge on careful training with amounts of expensive and
time-consuming labeled data. To alleviate labeled data scarcity, self-training
methods have been widely adopted on graphs by labeling high-confidence
unlabeled nodes and then adding them to the training step. In this line, we
empirically make a thorough study for current self-training methods on graphs.
Surprisingly, we find that high-confidence unlabeled nodes are not always
useful, and even introduce the distribution shift issue between the original
labeled dataset and the augmented dataset by self-training, severely hindering
the capability of self-training on graphs. To this end, in this paper, we
propose a novel Distribution Recovered Graph Self-Training framework (DR-GST),
which could recover the distribution of the original labeled dataset.
Specifically, we first prove the equality of loss function in self-training
framework under the distribution shift case and the population distribution if
each pseudo-labeled node is weighted by a proper coefficient. Considering the
intractability of the coefficient, we then propose to replace the coefficient
with the information gain after observing the same changing trend between them,
where information gain is respectively estimated via both dropout variational
inference and dropedge variational inference in DR-GST. However, such a
weighted loss function will enlarge the impact of incorrect pseudo labels. As a
result, we apply the loss correction method to improve the quality of pseudo
labels. Both our theoretical analysis and extensive experiments on five
benchmark datasets demonstrate the effectiveness of the proposed DR-GST, as
well as each well-designed component in DR-GST.

该论文提出了一种新的自我训练方法 (DR-GST)，通过 dropout 变分推理和 dropedge 变分推理来恢复标注数据集的分布，并利用 loss 校正方法来提高伪标签的质量，在五个基准数据集上都取得了良好的效果。