Beltrami Flow and Neural Diffusion on Graphs

18 October 2021

B. Chamberlain

J. Rowbottom

D. Eynard

Francesco Di Giovanni

Papers citing "Beltrami Flow and Neural Diffusion on Graphs"

27 / 27 papers shown

Title
Graph ODEs and Beyond: A Comprehensive Survey on Integrating Differential Equations with Graph Neural Networks Z. Liu Xiaoda Wang Bohan Wang Zijie Huang Carl Yang Wei-dong Jin AI4TS AI4CE 81 0 0 29 Mar 2025
Learning to Decouple Complex Systems Zihan Zhou Tianshu Yu BDL 64 4 0 17 Feb 2025
Understanding Oversmoothing in GNNs as Consensus in Opinion Dynamics Keqin Wang Yulong Yang Ishan Saha Christine Allen-Blanchette 51 1 0 31 Jan 2025
When Graph Neural Networks Meet Dynamic Mode Decomposition Dai Shi Lequan Lin Andi Han Zhiyong Wang Yi Guo Junbin Gao AI4CE 23 0 0 08 Oct 2024
A Learned Generalized Geodesic Distance Function-Based Approach for Node Feature Augmentation on Graphs Amitoz Azad Yuan Fang 32 1 0 01 Jul 2024
Bundle Neural Networks for message diffusion on graphs Jacob Bamberger Federico Barbero Xiaowen Dong Michael M. Bronstein 37 1 0 24 May 2024
Understanding Oversmoothing in Diffusion-Based GNNs From the Perspective of Operator Semigroup Theory Weichen Zhao Chenguang Wang Xinyan Wang Congying Han Tiande Guo Tianshu Yu 38 0 0 23 Feb 2024
Manifold GCN: Diffusion-based Convolutional Neural Network for Manifold-valued Graphs M. Hanik Gabriele Steidl C. V. Tycowicz GNN MedIm 19 3 0 25 Jan 2024
PosDiffNet: Positional Neural Diffusion for Point Cloud Registration in a Large Field of View with Perturbations Rui She Sijie Wang Qiyu Kang Kai Zhao Yang Song Wee Peng Tay Tianyu Geng Xingchao Jian DiffM 3DPC 32 2 0 06 Jan 2024
A Geometric Insight into Equivariant Message Passing Neural Networks on Riemannian Manifolds Ilyes Batatia 13 0 0 16 Oct 2023
Adversarial Robustness in Graph Neural Networks: A Hamiltonian Approach Kai Zhao Qiyu Kang Yang Song Rui She Sijie Wang Wee Peng Tay AAML 25 21 0 10 Oct 2023
Supercharging Graph Transformers with Advective Diffusion Qitian Wu Chenxiao Yang Kaipeng Zeng Fan Nie AI4CE 45 6 0 10 Oct 2023
Unifying over-smoothing and over-squashing in graph neural networks: A physics informed approach and beyond Zhiqi Shao Dai Shi Andi Han Yi Guo Qianchuan Zhao Junbin Gao 24 11 0 06 Sep 2023
QDC: Quantum Diffusion Convolution Kernels on Graphs Thomas Markovich GNN 10 3 0 20 Jul 2023
Revisiting Generalized p-Laplacian Regularized Framelet GCNs: Convergence, Energy Dynamic and Training with Non-Linear Diffusion Dai Shi Zhiqi Shao Yi Guo Qianchuan Zhao Junbin Gao 30 1 0 25 May 2023
Node Embedding from Hamiltonian Information Propagation in Graph Neural Networks Qiyu Kang Kai Zhao Yang Song Sijie Wang Rui She Wee Peng Tay 28 0 0 02 Mar 2023
TIDE: Time Derivative Diffusion for Deep Learning on Graphs M. Behmanesh Maximilian Krahn M. Ovsjanikov DiffM GNN 19 9 0 05 Dec 2022
Capturing Graphs with Hypo-Elliptic Diffusions Csaba Tóth Darrick Lee Celia Hacker Harald Oberhauser 16 12 0 27 May 2022
Graph-Coupled Oscillator Networks T. Konstantin Rusch B. Chamberlain J. Rowbottom S. Mishra M. Bronstein 31 101 0 04 Feb 2022
Heterogeneous manifolds for curvature-aware graph embedding Francesco Di Giovanni Giulia Luise M. Bronstein 49 23 0 02 Feb 2022
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges M. Bronstein Joan Bruna Taco S. Cohen Petar Velivcković GNN 172 1,100 0 27 Apr 2021
Graph Information Bottleneck Tailin Wu Hongyu Ren Pan Li J. Leskovec AAML 88 224 0 24 Oct 2020
Fourier Neural Operator for Parametric Partial Differential Equations Zong-Yi Li Nikola B. Kovachki Kamyar Azizzadenesheli Burigede Liu K. Bhattacharya Andrew M. Stuart Anima Anandkumar AI4CE 203 2,272 0 18 Oct 2020
Lagrangian Neural Networks M. Cranmer S. Greydanus Stephan Hoyer Peter W. Battaglia D. Spergel S. Ho PINN 121 419 0 10 Mar 2020
Learning Symbolic Physics with Graph Networks M. Cranmer Rui Xu Peter W. Battaglia S. Ho PINN AI4CE 180 83 0 12 Sep 2019
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation C. Qi Hao Su Kaichun Mo Leonidas J. Guibas 3DH 3DPC 3DV PINN 219 14,047 0 02 Dec 2016
Geometric deep learning on graphs and manifolds using mixture model CNNs Federico Monti Davide Boscaini Jonathan Masci Emanuele Rodolà Jan Svoboda M. Bronstein GNN 234 1,809 0 25 Nov 2016