mlsanityData to 14 Oct 2026

Wasserstein Adversarially Regularized Graph Autoencoder

arXiv 2111.04981 ↗First posted 9 Nov 2021cs.LGcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 18 of its 28 references that are in the map. First posted on arXiv 9 Nov 2021.

Builds on

18 of 28 references.

References · 18

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6 more
  1. 2018Sorting out Lipschitz function approximationcited by 213
  2. 2018Adversarially Regularized Graph Autoencoder for Graph Embeddingcited by 84
  3. 2019Graph Attention Auto-Encoderscited by 41
  4. 2021Rethinking Graph Auto-Encoder Models for Attributed Graph Clusteringcited by 37
  5. 2020Approximating Lipschitz continuous functions with GroupSort neural networkscited by 27
  6. 2017Large-scale analysis of disease pathways in the human interactomecited by 14

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 0; 2024: 0; 2025: 0; 2026: 1.

2 papers.

  1. 2022A Survey of Deep Graph Clustering: Taxonomy, Challenge, Application, and Open Resourcecited by 26
  2. 2026Beyond the Academic Monoculture: A Unified Framework and Industrial Perspective for Attributed Graph Clusteringcited by 1