arXiv Machine Learning June 30, 2026 By Eran Rosenbluth

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

Read the original on arXiv Machine Learning →

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).

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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 Machine Learning

Jun 9

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

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.

By Ofek Amran, Tom Gilat, Ron Levie

arXiv Machine Learning

Sep 1

Graph Representational Learning: When Does More Expressivity Hurt Generalization?

arXiv:2505.11298v2 Announce Type: replace Abstract: Graph Neural Networks (GNNs) are powerful tools for learning on structured data, yet the relationship between their expressivity and predictive per...

By Sohir Maskey, Raffaele Paolino, Fabian Jogl, Gitta Kutyniok, Johannes F. Lutzeyer

Hugging Face Trending Papers

Jun 11

Understanding Truncated Positional Encodings for Graph Neural Networks

Positional encodings (PEs) enhance the power of graph neural networks (GNNs), both theoretically and empirically. Two of the most popular families of PEs - spectral (e.

arXiv AI

Jun 2

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 AI

Aug 25

Which Algorithms Can Graph Neural Networks Learn?

arXiv:2602.13106v2 Announce Type: replace-cross Abstract: In recent years, there has been growing interest in understanding neural architectures' ability to learn to execute discrete algorithms, a li...

By Solveig Wittig, Antonis Vasileiou, Robert R. Nerem, Timo Stoll, Floris Geerts, Yusu Wang, Christopher Morris

efficiency

One email a morning, machine-written

Related stories

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

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

Graph Representational Learning: When Does More Expressivity Hurt Generalization?

Understanding Truncated Positional Encodings for Graph Neural Networks

Introduction to Graph Neural Networks for Machine Learning Engineers

Which Algorithms Can Graph Neural Networks Learn?