Key papers

• A user's guide to PINNs in geometric analysis: lessons from the asymptotic Plateau problem (2026)
  Tancredi Schettini Gherardini
  1.94
• Finding core subgraphs of directed graphs via discrete Ricci curvature flow (2025)
  Juan Zhao et al.
  1.56
• Convergence of graph Dirichlet energies and graph Laplacians on intersecting manifolds of varying dimensions (2025)
  Leon Bungert and Dejan Slep\v{c}ev
  1.39
• Momentum-based minimization of the Ginzburg-Landau functional on Euclidean spaces and graphs (2025)
  Oluwatosin Akande et al.
  0.94
• Hypergraph Clustering Based on PageRank (2020)
  Yuuki Takai et al.
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• Poisson Learning: Graph Based Semi-Supervised Learning At Very Low Label Rates (2020)
  Jeff Calder et al.
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• Lipschitz regularity of graph Laplacians on random data clouds (2020)
  Jeff Calder and Nicolas Garcia Trillos and Marta Lewicka
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• On Frink's type metrization of weighted graphs (2020)
  Mar\'ia Florencia Acosta and Hugo Aimar and Ivana G\'omez
  —
• Continuum Limit of Lipschitz Learning on Graphs (2020)
  Tim Roith et al.
  —
• LDLE: Low Distortion Local Eigenmaps (2021)
  Dhruv Kohli et al.
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• Clustering dynamics on graphs: from spectral clustering to mean shift through Fokker-Planck interpolation (2021)
  Katy Craig et al.
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• Uniform Convergence Rates for Lipschitz Learning on Graphs (2021)
  Leon Bungert et al.
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• Models for information propagation on graphs (2022)
  Oliver R. A. Dunbar et al.
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• Hamilton-Jacobi equations on graphs with applications to semi-supervised learning and data depth (2022)
  Jeff Calder et al.
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• Scalable algorithms for physics-informed neural and graph networks (2022)
  Khemraj Shukla et al.
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• ACMP: Allen-Cahn Message Passing with Attractive and Repulsive Forces for Graph Neural Networks (2022)
  Yuelin Wang et al.
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• Rates of Convergence for Regression with the Graph Poly-Laplacian (2022)
  Nicol\'as Garc\'ia Trillos et al.
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• Narrow escape in composite domains forming heterogeneous networks (2022)
  Fr\'ed\'eric Paquin-Lefebvre et al.
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• An Implicit GNN Solver for Poisson-like problems (2023)
  Matthieu Nastorg (TAU et al.
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• Physics-Informed Graph Convolutional Networks: Towards a generalized framework for complex geometries (2023)
  Marien Chenaud et al.
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• Consistency of semi-supervised learning, stochastic tug-of-war games, and the p-Laplacian (2024)
  Jeff Calder et al.
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• Continuum limit of $p$-biharmonic equations on graphs (2024)
  Kehan Shi et al.
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• Hypergraph $p$-Laplacian regularization on point clouds for data interpolation (2024)
  Kehan Shi et al.
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• Improved Graph-based semi-supervised learning Schemes (2024)
  Farid Bozorgnia
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• Convergence rates for Poisson learning to a Poisson equation with measure data (2024)
  Leon Bungert and Jeff Calder and Max Mihailescu and Kodjo Houssou and Amber Yuan
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