arXiv Machine Learning June 16, 2026 By Antonis Vasileiou, Juan Cervino, Pascal Frossard, Charilaos I. Kanatsoulis, Christopher Morris, Michael T. Schaub, Pierre Vandergheynst, Zhiyang Wang, Guy Wolf, Ron Levie

Graph Learning Should Move Beyond Restrictive Views of Spectral and Message-Passing GNNs

Read the original on arXiv Machine Learning →

arXiv:2602. 10031v2 Announce Type: replace Abstract: Graph neural networks (GNNs) are commonly divided into message-passing neural networks (MPNNs) and spectral GNNs, reflecting two largely separate research traditions in machine learning and signal processing.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Jun 2

Towards Stable, Globally Expressive Graph Representations with Laplacian Eigenvectors

arXiv:2410. 09737v2 Announce Type: replace Abstract: A popular way to improve the expressive power of graph neural networks (GNNs) is to use Laplacian eigenvectors as additional node features, since they can serve both as structural identifiers and global coordinates of nodes.

By Junru Zhou, Cai Zhou, Xiyuan Wang, Pan Li, Muhan Zhang

benchmarks

arXiv Machine Learning

Aug 4

Nonlinear Laplacians Improve Signed-Directed Graph Learning

arXiv:2608. 00836v1 Announce Type: new Abstract: While signed-directed graphs have been studied using linear Laplacians in the design of graph neural networks, relatively little research has focused on developing non-linear Laplacian operators for such networks.

By Ali Parviz, Yuichi Yoshida

Hugging Face Trending Papers

Jul 7

Graph Convolutional Attention: A Spectral Perspective on Graph Denoising and Diffusion

Denoising graphs is a fundamental problem in graph learning and the core operation of graph diffusion models. Attention-based architectures like graph transformers have recently shown promise in denoising graphs.

llms diffusion

arXiv AI

Jul 8

Graph Convolutional Attention: A Spectral Perspective on Graph Denoising and Diffusion

arXiv:2607. 06546v1 Announce Type: cross Abstract: Denoising graphs is a fundamental problem in graph learning and the core operation of graph diffusion models.

By Shervin Khalafi, Igor Krawczuk, Sergio Rozada, Charilaos Kanatsoulis, Antonio G Marques, Alejandro Ribeiro

llms diffusion

arXiv Machine Learning

Jun 25

Convex--Concave Quadratic Spectral Filtering for Graph Neural Networks

arXiv:2606. 24956v1 Announce Type: new Abstract: Spectral graph neural networks (GNNs) interpret message passing as frequency-selective filtering.

By Ranhui Yan, Jia Cai, Mengzhu Chen, Haodong Yang

benchmarks

arXiv Machine Learning

Jun 3

A Graph Foundation Model with Spectral Parsing and Prototype-Guided Spatial Propagation

arXiv:2606. 03315v1 Announce Type: new Abstract: Graph foundation models aim to learn transferable knowledge from diverse graphs for generalization to unseen graphs and tasks.

By Ankang Yang, Jitao Zhao, Dongxiao He, Liang Yang, Di Jin, Weixiong Zhang

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Towards Stable, Globally Expressive Graph Representations with Laplacian Eigenvectors

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