Understanding Graph Neural Networks with Generalized Geometric Scattering Transforms-Reference-Cited by-同舟云学术

Understanding Graph Neural Networks with Generalized Geometric Scattering Transforms

Published:2023-10-25 Issue:4 Volume:5 Page:873-898
ISSN:2577-0187
Container-title:SIAM Journal on Mathematics of Data Science
language:en
Short-container-title:SIAM Journal on Mathematics of Data Science

Author:

Perlmutter Michael¹,Tong Alexander²,Gao Feng³,Wolf Guy⁴^ORCID,Hirn Matthew⁵^ORCID

Affiliation:

1. Boise State University, Department of Mathematics, 1910 University Drive, Boise, ID 83706 USA.

2. Department of Computer Science and Operations Research, Université de Montréal, Montréal, QC H3T 1J4, Canada, and Mila - Quebec AI Institute, Montréal, QC H2S 3H1, Canada.

3. Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY 10032 USA.

4. Department of Mathematics and Statistics, Université de Montréal, Montréal, QC H3T 1J4, Canada, and Mila - Quebec AI Institute, Montréal, QC H2S 3H1, Canada.

5. Department of Computational Mathematics, Science, and Engineering and Department of Mathematics, Michigan State University, East Lansing, MI 48824 USA.

Funder

IVADO

Canada CIFAR AI Chair

Fonds de recherche du Québec – Nature et technologies

National Institutes of Health

National Science Foundation

Publisher

Society for Industrial & Applied Mathematics (SIAM)

Subject

General Medicine

Reference15 articles.

1. A graph discretization of the Laplace–Beltrami operator

2. Deep Haar scattering networks

3. Diffusion maps

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