  Recent efforts show that neural networks are vulnerable to small but
intentional perturbations on input features in visual classification tasks. Due
to the additional consideration of connections between examples (\eg articles
with citation link tend to be in the same class), graph neural networks could
be more sensitive to the perturbations, since the perturbations from connected
examples exacerbate the impact on a target example. Adversarial Training (AT),
a dynamic regularization technique, can resist the worst-case perturbations on
input features and is a promising choice to improve model robustness and
generalization. However, existing AT methods focus on standard classification,
being less effective when training models on graph since it does not model the
impact from connected examples.
  In this work, we explore adversarial training on graph, aiming to improve the
robustness and generalization of models learned on graph. We propose Graph
Adversarial Training (GraphAT), which takes the impact from connected examples
into account when learning to construct and resist perturbations. We give a
general formulation of GraphAT, which can be seen as a dynamic regularization
scheme based on the graph structure. To demonstrate the utility of GraphAT, we
employ it on a state-of-the-art graph neural network model --- Graph
Convolutional Network (GCN). We conduct experiments on two citation graphs
(Citeseer and Cora) and a knowledge graph (NELL), verifying the effectiveness
of GraphAT which outperforms normal training on GCN by 4.51% in node
classification accuracy. Codes are available via:
https://github.com/fulifeng/GraphAT.
