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arXiv · 2111.04981

Wasserstein Adversarially Regularized Graph Autoencoder

Huidong Liang·Junbin Gao
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Abstract

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.

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Huidong Liang, Junbin Gao. 2021-11-09. Wasserstein Adversarially Regularized Graph Autoencoder. https://arxiv.org/abs/2111.04981

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