Papers

• Any-Dimensional Invariant Universality (2026)
  Shengtai Yao et al.
  1.83
• Manifold limit for the training of shallow graph convolutional neural networks (2026)
  Johanna Tengler et al.
  1.61
• Quantitative Bounds for Sorting-Based Permutation-Invariant Embeddings (2025)
  Nadav Dym and Matthias Wellershoff and Efstratios Tsoukanis and Daniel Levy and Radu Balan
  1.44
• Metric spaces of walks and Lipschitz duality on graphs (2025)
  R. Arnau et al.
  1.33
• Data-Adaptive Graph Framelets with Generalized Vanishing Moments for Graph Machine Learning (2023)
  Ruigang Zheng and Xiaosheng Zhuang
  0.17
• Hypergraph Clustering Based on PageRank (2020)
  Yuuki Takai et al.
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• Construction and Monte Carlo estimation of wavelet frames generated by a reproducing kernel (2020)
  Ernesto De Vito et al.
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• Quantitative approximation results for complex-valued neural networks (2021)
  A. Caragea et al.
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• Measuring Segregation via Analysis on Graphs (2021)
  Moon Duchin et al.
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• Permutation Invariant Representations with Applications to Graph Deep Learning (2022)
  Radu Balan et al.
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• Limitless stability for Graph Convolutional Networks (2023)
  Christian Koke
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• Permutation Equivariant Graph Framelets for Heterophilous Graph Learning (2023)
  Jianfei Li et al.
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• Bridging Smoothness and Approximation: Theoretical Insights into Over-Smoothing in Graph Neural Networks (2024)
  Guangrui Yang et al.
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• Ginzburg--Landau Functionals in the Large-Graph Limit (2024)
  Edith Zhang et al.
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