  In this paper, we study the robustness of graph convolutional networks
(GCNs). Despite the good performance of GCNs on graph semi-supervised learning
tasks, previous works have shown that the original GCNs are very unstable to
adversarial perturbations. In particular, we can observe a severe performance
degradation by slightly changing the graph adjacency matrix or the features of
a few nodes, making it unsuitable for security-critical applications. Inspired
by the previous works on adversarial defense for deep neural networks, and
especially adversarial training algorithm, we propose a method called
GraphDefense to defend against the adversarial perturbations. In addition, for
our defense method, we could still maintain semi-supervised learning settings,
without a large label rate. We also show that adversarial training in features
is equivalent to adversarial training for edges with a small perturbation. Our
experiments show that the proposed defense methods successfully increase the
robustness of Graph Convolutional Networks. Furthermore, we show that with
careful design, our proposed algorithm can scale to large graphs, such as
Reddit dataset.
