  Deep learning models for graphs, especially Graph Convolutional Networks
(GCNs), have achieved remarkable performance in the task of semi-supervised
node classification. However, recent studies show that GCNs suffer from
adversarial perturbations. Such vulnerability to adversarial attacks
significantly decreases the stability of GCNs when being applied to
security-critical applications. Defense methods such as preprocessing,
attention mechanism and adversarial training have been discussed by various
studies. While being able to achieve desirable performance when the
perturbation rates are low, such methods are still vulnerable to high
perturbation rates. Meanwhile, some defending algorithms perform poorly when
the node features are not visible. Therefore, in this paper, we propose a novel
mechanism called influence mechanism, which is able to enhance the robustness
of the GCNs significantly. The influence mechanism divides the effect of each
node into two parts: introverted influence which tries to maintain its own
features and extroverted influence which exerts influences on other nodes.
Utilizing the influence mechanism, we propose the Influence GCN (I-GCN) model.
Extensive experiments show that our proposed model is able to achieve higher
accuracy rates than state-of-the-art methods when defending against
non-targeted attacks.
