  The effectiveness of Graph Convolutional Networks (GCNs) has been
demonstrated in a wide range of graph-based machine learning tasks. However,
the update of parameters in GCNs is only from labeled nodes, lacking the
utilization of unlabeled data. In this paper, we apply Virtual Adversarial
Training (VAT), an adversarial regularization method based on both labeled and
unlabeled data, on the supervised loss of GCN to enhance its generalization
performance. By imposing virtually adversarial smoothness on the posterior
distribution in semi-supervised learning, VAT yields improvement on the
Symmetrical Laplacian Smoothness of GCNs. In addition, due to the difference of
property in features, we perturb virtual adversarial perturbations on sparse
and dense features, resulting in GCN Sparse VAT (GCNSVAT) and GCN Dense VAT
(GCNDVAT) algorithms, respectively. Extensive experiments verify the
effectiveness of our two methods across different training sizes. Our work
paves the way towards better understanding the direction of improvement on GCNs
in the future.
