  We present batch virtual adversarial training (BVAT), a novel regularization
method for graph convolutional networks (GCNs). BVAT addresses the shortcoming
of GCNs that do not consider the smoothness of the model's output distribution
against local perturbations around the input. We propose two algorithms,
sample-based BVAT and optimization-based BVAT, which are suitable to promote
the smoothness of the model for graph-structured data by either finding virtual
adversarial perturbations for a subset of nodes far from each other or
generating virtual adversarial perturbations for all nodes with an optimization
process. Extensive experiments on three citation network datasets Cora,
Citeseer and Pubmed and a knowledge graph dataset Nell validate the
effectiveness of the proposed method, which establishes state-of-the-art
results in the semi-supervised node classification tasks.
