Cover Image

Equivariant Machine Learning on Graphs with Nonlinear Spectral Filters

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Lin, Ya-Wei Eileen, Talmon, Ronen, Levie, Ron
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Adaptive Canonicalization with Application to Invariant Anisotropic Geometric Networks
by: Lin, Ya-Wei Eileen, et al.
Published: (2025)

Beyond Oversquashing: Understanding Signal Propagation in GNNs Via Observables
by: Nagar, Eden, et al.
Published: (2026)

Joint Hierarchical Representation Learning of Samples and Features via Informed Tree-Wasserstein Distance
by: Lin, Ya-Wei Eileen, et al.
Published: (2025)

Tree-Wasserstein Distance for High Dimensional Data with a Latent Feature Hierarchy
by: Lin, Ya-Wei Eileen, et al.
Published: (2024)

A Note on Graphon-Signal Analysis of Graph Neural Networks
by: Rauchwerger, Levi, et al.
Published: (2025)

It Takes a Graph to Know a Graph: Rewiring for Homophily with a Reference Graph
by: Mendelman, Harel, et al.
Published: (2025)

Equivariance Everywhere All At Once: A Recipe for Graph Foundation Models
by: Finkelshtein, Ben, et al.
Published: (2025)

Generalization, Expressivity, and Universality of Graph Neural Networks on Attributed Graphs
by: Rauchwerger, Levi, et al.
Published: (2024)

PieClam: A Universal Graph Autoencoder Based on Overlapping Inclusive and Exclusive Communities
by: Zilberg, Daniel, et al.
Published: (2024)

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation
by: Amran, Ofek, et al.
Published: (2026)

LieAugmenter: Equivariant Learning by Discovering Symmetries with Learnable Augmentations
by: Santos-Escriche, Eduardo, et al.
Published: (2025)

Complex Interpolation of Matrices with an application to Multi-Manifold Learning
by: Arbel, Adi, et al.
Published: (2026)

Efficient Learning on Large Graphs using a Densifying Regularity Lemma
by: Kouchly, Jonathan, et al.
Published: (2025)

Learning on Large Graphs using Intersecting Communities
by: Finkelshtein, Ben, et al.
Published: (2024)

Survey on Generalization Theory for Graph Neural Networks
by: Vasileiou, Antonis, et al.
Published: (2025)

Generalization Bounds for Message Passing Networks on Mixture of Graphons
by: Maskey, Sohir, et al.
Published: (2024)

Neural Networks With Dense Weights Are Not Universal Approximators
by: Rauchwerger, Levi, et al.
Published: (2026)

Enhancing VICReg: Random-Walk Pairing for Improved Generalization and Better Global Semantics Capturing
by: Simai, Idan, et al.
Published: (2025)

The Expected Loss of Preconditioned Langevin Dynamics Reveals the Hessian Rank
by: Bar, Amitay, et al.
Published: (2024)

Learning Shared Representations from Unpaired Data
by: Yacobi, Amitai, et al.
Published: (2025)

Future Directions in the Theory of Graph Machine Learning
by: Morris, Christopher, et al.
Published: (2024)

Dataset of Pathloss and ToA Radio Maps With Localization Application
by: Yapar, Çağkan, et al.
Published: (2022)

Graph Filters for Signal Processing and Machine Learning on Graphs
by: Isufi, Elvin, et al.
Published: (2022)

Weisfeiler Leman for Euclidean Equivariant Machine Learning
by: Hordan, Snir, et al.
Published: (2024)

HeroFilter: Adaptive Spectral Graph Filter for Varying Heterophilic Relations
by: Zhang, Shuaicheng, et al.
Published: (2025)

Graph Spectral Filtering with Chebyshev Interpolation for Recommendation
by: Kim, Chanwoo, et al.
Published: (2025)

Data-Driven Graph Filters via Adaptive Spectral Shaping
by: Sandfelder, Dylan, et al.
Published: (2026)

SpectralNet: Spectral Clustering using Deep Neural Networks
by: Shaham, Uri, et al.
Published: (2018)

Graph Neural Networks with Diverse Spectral Filtering
by: Guo, Jingwei, et al.
Published: (2023)

Elign: Equivariant Diffusion Model Alignment from Foundational Machine Learning Force Fields
by: Li, Yunyang, et al.
Published: (2026)

Covered Forest: Fine-grained generalization analysis of graph neural networks
by: Vasileiou, Antonis, et al.
Published: (2024)

DeepChem Equivariant: SE(3)-Equivariant Support in an Open-Source Molecular Machine Learning Library
by: Siguenza, Jose, et al.
Published: (2025)

On Learning what to Learn: heterogeneous observations of dynamics and establishing (possibly causal) relations among them
by: Sroczynski, David W., et al.
Published: (2024)

Solving Partial Differential Equations with Equivariant Extreme Learning Machines
by: Harder, Hans, et al.
Published: (2024)

Morphological-Symmetry-Equivariant Heterogeneous Graph Neural Network for Robotic Dynamics Learning
by: Xie, Fengze, et al.
Published: (2024)

Scale Equivariant Graph Metanetworks
by: Kalogeropoulos, Ioannis, et al.
Published: (2024)

Muon with Spectral Guidance: Efficient Optimization for Scientific Machine Learning
by: Lu, Binghang, et al.
Published: (2026)

Rethinking Spectral Graph Neural Networks with Spatially Adaptive Filtering
by: Guo, Jingwei, et al.
Published: (2024)

Bloom Filter Encoding for Machine Learning
by: Cartmell, John, et al.
Published: (2025)

Finsler Multi-Dimensional Scaling: Manifold Learning for Asymmetric Dimensionality Reduction and Embedding
by: Dagès, Thomas, et al.
Published: (2025)