  This paper introduces Wasserstein Adversarially Regularized Graph Autoencoder
(WARGA), an implicit generative algorithm that directly regularizes the latent
distribution of node embedding to a target distribution via the Wasserstein
metric. The proposed method has been validated in tasks of link prediction and
node clustering on real-world graphs, in which WARGA generally outperforms
state-of-the-art models based on Kullback-Leibler (KL) divergence and typical
adversarial framework.
