arXiv Machine Learning June 9, 2026 By Ofek Amran, Tom Gilat, Ron Levie

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation

Read the original on arXiv Machine Learning →

arXiv:2602. 08785v2 Announce Type: replace Abstract: Generalization and approximation capabilities of message passing graph neural networks (MPNNs) are often studied by defining a compact metric on a space of input graphs under which MPNNs are equicontinuous.

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

arXiv Machine Learning

Jun 30

Lost in Aggregation: On a Fundamental Expressivity Limit of Message-Passing Graph Neural Networks

arXiv:2603. 14846v3 Announce Type: replace Abstract: We define an information-complexity property for aggregation functions, capturing a vast range of practical aggregations, and prove that any Message-Passing Graph Neural Network (MP-GNN) model with such aggregations induces only a polynomial number of equivalence classes on all graphs - while the number of non-isomorphic graphs is super-exponential (in number of vertices).

By Eran Rosenbluth

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Universality and Approximation Rates of Graph Neural Networks with Random Features

We investigate message-passing graph neural networks with random node features. Random node features are known to enhance the expressiveness of graph neural networks (GNNs) both theoretically and empirically.

arXiv Machine Learning

Jul 30

On the Rademacher Complexity of Graph Neural Networks: Unifying Expressivity and Geometry

arXiv:2510. 10101v4 Announce Type: replace Abstract: Understanding the interplay between generalization, expressivity, and the geometry of the input space is a central challenge in graph learning.

By Martin Carrasco, Caio F. Deberaldini Netto, Vahan A. Martirosyan, Ehimare Okoyomon, Caterina Graziani

arXiv AI

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Learning to Execute Graph Algorithms Exactly with Graph Neural Networks

arXiv:2601. 23207v2 Announce Type: replace-cross Abstract: Understanding what graph neural networks can learn, especially their ability to learn to execute algorithms, remains a central theoretical challenge.

By Muhammad Fetrat Qharabagh, Artur Back de Luca, George Giapitzakis, Kimon Fountoulakis

arXiv AI

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Introduction to Graph Neural Networks for Machine Learning Engineers

arXiv:2412. 19419v2 Announce Type: replace-cross Abstract: Graph neural networks are deep neural networks designed for graphs with attributes attached to nodes or edges.

By James H. Tanis, Chris Giannella, Adrian V. Mariano, Daoud Meerzaman

arXiv Machine Learning

Jul 30

Universality and Approximation Rates of Graph Neural Networks with Random Features

arXiv:2607. 26699v1 Announce Type: new Abstract: We investigate message-passing graph neural networks with random node features.

By Lukas Gonon, Thilo Meyer-Brandis, Niklas Weber

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Lost in Aggregation: On a Fundamental Expressivity Limit of Message-Passing Graph Neural Networks

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