Paper ID: 2111.04981

Wasserstein Adversarially Regularized Graph Autoencoder

Huidong Liang, Junbin Gao

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.

Submitted: Nov 9, 2021