Papers

• A user's guide to PINNs in geometric analysis: lessons from the asymptotic Plateau problem (2026)
  Tancredi Schettini Gherardini
  1.94
• Neural Point-Forms (2026)
  Bruno Trentini et al.
  1.83
• Ollivier-Ricci Curvature of Riemannian Manifolds and Directed Graphs with Applications to Graph Neural Networks (2026)
  Eleanor Wiesler
  1.78
• Aligning the Unseen in Attributed Graphs: Interplay between Graph Geometry and Node Attributes Manifold (2026)
  Aldric Labarthe (CB et al.
  1.61
• Neural Feature Geometry Evolves as Discrete Ricci Flow (2025)
  Moritz Hehl et al.
  1.39
• Barycentric subspace analysis of network-valued data (2025)
  Elodie Maignant (UniCA et al.
  1.28
• Ergodic Generative Flows (2025)
  Leo Maxime Brunswic et al.
  1.17
• Graph Attention for Heterogeneous Graphs with Positional Encoding (2025)
  Nikhil Shivakumar Nayak
  1.11
• Geometric Sampling of Networks (2020)
  Vladislav Barkanass et al.
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• Geometry and Generalization: Eigenvalues as predictors of where a network will fail to generalize (2021)
  Susama Agarwala et al.
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• Large sample spectral analysis of graph-based multi-manifold clustering (2021)
  Nicolas Garcia Trillos et al.
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• SE(3) Equivariant Graph Neural Networks with Complete Local Frames (2021)
  Weitao Du et al.
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• Sheaf Neural Networks with Connection Laplacians (2022)
  Federico Barbero et al.
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• Exploring Singularities in point clouds with the graph Laplacian: An explicit approach (2023)
  Martin Andersson and Benny Avelin
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• Root Laplacian Eigenmaps with their application in spectral embedding (2023)
  Shouvik Datta Choudhury
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• Revealing Decurve Flows for Generalized Graph Propagation (2024)
  Chen Lin et al.
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• On the Relation between Graph Ricci Curvature and Community Structure (2024)
  Sathyanarayanan Rengaswami et al.
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