Understanding Graph Neural Networks with Generalized Geometric Scattering Transforms

DOI10.1137/21M1465056arXiv1911.06253OpenAlexW4387940089MaRDI QIDQ6070299

No author found.

Publication date: 20 November 2023

Published in: SIAM Journal on Mathematics of Data Science (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1911.06253

zbMATH Keywords

waveletsscattering transformgeometric deep learninggraph neural networks

Mathematics Subject Classification ID

Nontrigonometric harmonic analysis involving wavelets and other special systems (42C40) Artificial neural networks and deep learning (68T07) Graph theory (including graph drawing) in computer science (68R10) General harmonic expansions, frames (42C15) Graph representations (geometric and intersection representations, etc.) (05C62) Random walks on graphs (05C81)

Cites Work

• Unnamed Item
• A graph discretization of the Laplace-Beltrami operator
• Error estimates for spectral convergence of the graph Laplacian on random geometric graphs toward the Laplace-Beltrami operator
• Graph convolutional neural networks via scattering
• Diffusion maps
• Diffusion maps, spectral clustering and reaction coordinates of dynamical systems
• From graph to manifold Laplacian: the convergence rate
• Group Invariant Scattering
• Deep Haar scattering networks
• CayleyNets: Graph Convolutional Neural Networks With Complex Rational Spectral Filters
• Eigenvalues of Laplacians on a Closed Riemannian Manifold and Its Nets
• Wavelet Scattering Regression of Quantum Chemical Energies

This page was built for publication: Understanding Graph Neural Networks with Generalized Geometric Scattering Transforms