  Graph neural networks exhibit remarkable performance in graph data analysis.
However, the robustness of GNN models remains a challenge. As a result, they
are not reliable enough to be deployed in critical applications. Recent studies
demonstrate that GNNs could be easily fooled with adversarial perturbations,
especially structural perturbations. Such vulnerability is attributed to the
excessive dependence on the structure information to make predictions. To
achieve better robustness, it is desirable to build the prediction of GNNs with
more comprehensive features. Graph data, in most cases, has two views of
information, namely structure information and feature information. In this
paper, we propose CoG, a simple yet effective co-training framework to combine
these two views for the purpose of robustness. CoG trains sub-models from the
feature view and the structure view independently and allows them to distill
knowledge from each other by adding their most confident unlabeled data into
the training set. The orthogonality of these two views diversifies the
sub-models, thus enhancing the robustness of their ensemble. We evaluate our
framework on three popular datasets, and results show that CoG significantly
improves the robustness of graph models against adversarial attacks without
sacrificing their performance on clean data. We also show that CoG still
achieves good robustness when both node features and graph structures are
perturbed.
