mlsanityData to 7 Oct 2026

Curvature Graph Generative Adversarial Networks

arXiv 2203.01604 ↗First posted 3 Mar 2022cs.LG cs.SIcited by 9

Not on the map. Why?

Abstract (arXiv)

Generative adversarial network (GAN) is widely used for generalized and robust learning on graph data. However, for non-Euclidean graph data, the existing GAN-based graph representation methods generate negative samples by random walk or traverse in discrete space, leading to the information loss of topological properties (e.g. hierarchy and circularity). Moreover, due to the topological heterogeneity (i.e., different densities across the graph structure) of graph data, they suffer from serious topological distortion problems. In this paper, we proposed a novel Curvature Graph Generative Adversarial Networks method, named modelname, which is the first GAN-based graph representation method in the Riemannian geometric manifold. To better preserve the topological properties, we approximate the discrete structure as a continuous Riemannian geometric manifold and generate negative samples efficiently from the wrapped normal distribution. To deal with the topological heterogeneity, we leverage the Ricci curvature for local structures with different topological properties, obtaining to low-distortion representations. Extensive experiments show that CurvGAN consistently and significantly outperforms the state-of-the-art methods across multiple tasks and shows superior robustness and generalization.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 37 of its 70 references that are in the map. First posted on arXiv 3 Mar 2022.

Builds on

37 of 70 references.

References · 37

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25 more
  1. 2018Poincar'e GloVe: Hyperbolic Word Embeddingscited by 155
  2. 2017GraphGAN: Graph Representation Learning with Generative Adversarial Netscited by 121
  3. 2019Continuous Hierarchical Representations with Poincar'e Variational Auto-Encoderscited by 111
  4. 2019Constant Curvature Graph Convolutional Networkscited by 106
  5. 2017Detection and Resolution of Rumours in Social Media: A Surveycited by 103
  6. 2019Mixed-curvature Variational Autoencoderscited by 85
  7. 2021Graph Structure Learning with Variational Information Bottleneckcited by 81
  8. 2020DeepRobust: A PyTorch Library for Adversarial Attacks and Defensescited by 78
  9. 2021SUGAR: Subgraph Neural Network with Reinforcement Pooling and Self-Supervised Mutual Information Mechanismcited by 75
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  11. 2021A Comprehensive Survey on Community Detection with Deep Learningcited by 63
  12. 2021Hyperbolic Variational Graph Neural Network for Modeling Dynamic Graphscited by 49
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  14. 2021Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networkscited by 38
  15. 2021A Self-supervised Mixed-curvature Graph Neural Networkcited by 32
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  18. 2021ACE-HGNN: Adaptive Curvature Exploration Hyperbolic Graph Neural Networkcited by 24
  19. 2015Ricci Curvature of the Internet Topologycited by 22
  20. 2019DeepSphere: towards an equivariant graph-based spherical CNNcited by 13
  21. 2018Adversarial Autoencoders with Constant-Curvature Latent Manifoldscited by 12
  22. 2020Adversarial Directed Graph Embeddingcited by 12
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  25. 2019A Wrapped Normal Distribution on Hyperbolic Space for Gradient-Based Learningcited by 6

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 5; 2024: 0; 2025: 1; 2026: 1.

9 papers.

  1. 2022Position-aware Structure Learning for Graph Topology-imbalance by Relieving Under-reaching and Over-squashingcited by 31
  2. 2023Hyperbolic Geometric Graph Representation Learning for Hierarchy-imbalance Node Classificationcited by 23
  3. 2023Improving Heterogeneous Graph Learning with Weighted Mixed-Curvature Product Manifoldcited by 4
  4. 2022Hyperbolic Graph Representation Learning: A Tutorialcited by 2
  5. 2023Torsion Graph Neural Networkscited by 1
  6. 2023HAT-GAE: Self-Supervised Graph Auto-encoders with Hierarchical Adaptive Masking and Trainable Corruptioncited by 0
  7. 2023Curvature-enhanced Graph Convolutional Network for Biomolecular Interaction Predictioncited by 0
  8. 2025A roadmap for curvature-based geometric data analysis and learningcited by 0
  9. 2026Zero-shot Generalizable Graph Anomaly Detection with Mixture of Riemannian Expertscited by 0