  Adversarial training is an approach for increasing model's resilience against
adversarial perturbations. Such approaches have been demonstrated to result in
models with feature representations that generalize better. However, limited
works have been done on adversarial training of models on graph data. In this
paper, we raise such a question { does adversarial training improve the
generalization of graph representations. We formulate L2 and L1 versions of
adversarial training in two powerful node embedding methods: graph autoencoder
(GAE) and variational graph autoencoder (VGAE). We conduct extensive
experiments on three main applications, i.e. link prediction, node clustering,
graph anomaly detection of GAE and VGAE, and demonstrate that both L2 and L1
adversarial training boost the generalization of GAE and VGAE.
