  Graph representation learning is a fundamental research issue in various
domains of applications, of which the inductive learning problem is
particularly challenging as it requires models to generalize to unseen graph
structures during inference. In recent years, graph neural networks (GNNs) have
emerged as powerful graph models for inductive learning tasks such as node
classification, whereas they typically heavily rely on the annotated nodes
under a fully supervised training setting. Compared with the GNN-based methods,
variational graph auto-encoders (VGAEs) are known to be more generalizable to
capture the internal structural information of graphs independent of node
labels and have achieved prominent performance on multiple unsupervised
learning tasks. However, so far there is still a lack of work focusing on
leveraging the VGAE framework for inductive learning, due to the difficulties
in training the model in a supervised manner and avoiding over-fitting the
proximity information of graphs. To solve these problems and improve the model
performance of VGAEs for inductive graph representation learning, in this work,
we propose the Self-Label Augmented VGAE model. To leverage the label
information for training, our model takes node labels as one-hot encoded inputs
and then performs label reconstruction in model training. To overcome the
scarcity problem of node labels for semi-supervised settings, we further
propose the Self-Label Augmentation Method (SLAM), which uses pseudo labels
generated by our model with a node-wise masking approach to enhance the label
information. Experiments on benchmark inductive learning graph datasets verify
that our proposed model archives promising results on node classification with
particular superiority under semi-supervised learning settings.
