Wasserstein Adversarially Regularized Graph Autoencoder

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-ofthe-art models based on Kullback-Leibler (KL) divergence and typical adversarial framework.

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