Computationally Tractable Riemannian Manifolds for Graph Embeddings

20 February 2020

Papers citing "Computationally Tractable Riemannian Manifolds for Graph Embeddings"

10 / 10 papers shown

Title
Pulling back symmetric Riemannian geometry for data analysis W. Diepeveen 19 2 0 11 Mar 2024
Manifold GCN: Diffusion-based Convolutional Neural Network for Manifold-valued Graphs M. Hanik Gabriele Steidl C. V. Tycowicz GNN MedIm 36 3 0 25 Jan 2024
Contrastive Graph Clustering in Curvature Spaces Li Sun Feiyang Wang Junda Ye Hao Peng Philip S. Yu 29 17 0 05 May 2023
Self-Supervised Continual Graph Learning in Adaptive Riemannian Spaces Li Sun Junda Ye Hao Peng Feiyang Wang Philip S. Yu CLL 19 31 0 30 Nov 2022
Fully-Connected Network on Noncompact Symmetric Space and Ridgelet Transform based on Helgason-Fourier Analysis Sho Sonoda Isao Ishikawa Masahiro Ikeda 21 15 0 03 Mar 2022
Heterogeneous manifolds for curvature-aware graph embedding Francesco Di Giovanni Giulia Luise M. Bronstein 64 23 0 02 Feb 2022
A Self-supervised Mixed-curvature Graph Neural Network Li Sun Zhongbao Zhang Junda Ye Hao Peng Jiawei Zhang Sen Su Philip S. Yu SSL 33 33 0 10 Dec 2021
Vector-valued Distance and Gyrocalculus on the Space of Symmetric Positive Definite Matrices F. López Beatrice Pozzetti Steve J. Trettel Michael Strube Anna Wienhard 39 17 0 26 Oct 2021
On Riemannian Optimization over Positive Definite Matrices with the Bures-Wasserstein Geometry Andi Han Bamdev Mishra Pratik Jawanpuria Junbin Gao 11 38 0 01 Jun 2021
Geometric deep learning: going beyond Euclidean data M. Bronstein Joan Bruna Yann LeCun Arthur Szlam P. Vandergheynst GNN 259 3,240 0 24 Nov 2016