Generalization, Expressivity, and Universality of Graph Neural Networks on Attributed Graphs
Fuente:
arXiv
Saved in:
| Main Authors: | Rauchwerger, Levi, Jegelka, Stefanie, Levie, Ron |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Neural Networks With Dense Weights Are Not Universal Approximators
by: Rauchwerger, Levi, et al.
Published: (2026)
by: Rauchwerger, Levi, et al.
Published: (2026)
A Note on Graphon-Signal Analysis of Graph Neural Networks
by: Rauchwerger, Levi, et al.
Published: (2025)
by: Rauchwerger, Levi, et al.
Published: (2025)
Survey on Generalization Theory for Graph Neural Networks
by: Vasileiou, Antonis, et al.
Published: (2025)
by: Vasileiou, Antonis, et al.
Published: (2025)
A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation
by: Amran, Ofek, et al.
Published: (2026)
by: Amran, Ofek, et al.
Published: (2026)
PieClam: A Universal Graph Autoencoder Based on Overlapping Inclusive and Exclusive Communities
by: Zilberg, Daniel, et al.
Published: (2024)
by: Zilberg, Daniel, et al.
Published: (2024)
Counting Substructures with Higher-Order Graph Neural Networks: Possibility and Impossibility Results
by: Tahmasebi, Behrooz, et al.
Published: (2020)
by: Tahmasebi, Behrooz, et al.
Published: (2020)
On the Stability of Expressive Positional Encodings for Graphs
by: Huang, Yinan, et al.
Published: (2023)
by: Huang, Yinan, et al.
Published: (2023)
Generalization Bounds for Message Passing Networks on Mixture of Graphons
by: Maskey, Sohir, et al.
Published: (2024)
by: Maskey, Sohir, et al.
Published: (2024)
Equivariant Machine Learning on Graphs with Nonlinear Spectral Filters
by: Lin, Ya-Wei Eileen, et al.
Published: (2024)
by: Lin, Ya-Wei Eileen, et al.
Published: (2024)
Higher-Order Graphon Neural Networks: Approximation and Cut Distance
by: Herbst, Daniel, et al.
Published: (2025)
by: Herbst, Daniel, et al.
Published: (2025)
Learning to Approximate Uniform Facility Location via Graph Neural Networks
by: Qian, Chendi, et al.
Published: (2026)
by: Qian, Chendi, et al.
Published: (2026)
Are Graph Neural Networks Optimal Approximation Algorithms?
by: Yau, Morris, et al.
Published: (2023)
by: Yau, Morris, et al.
Published: (2023)
Future Directions in the Theory of Graph Machine Learning
by: Morris, Christopher, et al.
Published: (2024)
by: Morris, Christopher, et al.
Published: (2024)
Towards Bridging Generalization and Expressivity of Graph Neural Networks
by: Li, Shouheng, et al.
Published: (2024)
by: Li, Shouheng, et al.
Published: (2024)
Efficient Learning on Large Graphs using a Densifying Regularity Lemma
by: Kouchly, Jonathan, et al.
Published: (2025)
by: Kouchly, Jonathan, et al.
Published: (2025)
Adaptive Canonicalization with Application to Invariant Anisotropic Geometric Networks
by: Lin, Ya-Wei Eileen, et al.
Published: (2025)
by: Lin, Ya-Wei Eileen, et al.
Published: (2025)
Learning on Large Graphs using Intersecting Communities
by: Finkelshtein, Ben, et al.
Published: (2024)
by: Finkelshtein, Ben, et al.
Published: (2024)
A Poincaré Inequality and Consistency Results for Signal Sampling on Large Graphs
by: Le, Thien, et al.
Published: (2023)
by: Le, Thien, et al.
Published: (2023)
Weisfeiler-Lehman goes Dynamic: An Analysis of the Expressive Power of Graph Neural Networks for Attributed and Dynamic Graphs
by: Beddar-Wiesing, Silvia, et al.
Published: (2022)
by: Beddar-Wiesing, Silvia, et al.
Published: (2022)
Equivariance Everywhere All At Once: A Recipe for Graph Foundation Models
by: Finkelshtein, Ben, et al.
Published: (2025)
by: Finkelshtein, Ben, et al.
Published: (2025)
Learning Efficient Positional Encodings with Graph Neural Networks
by: Kanatsoulis, Charilaos I., et al.
Published: (2025)
by: Kanatsoulis, Charilaos I., et al.
Published: (2025)
On the Expressive Power of Graph Neural Networks
by: Nalwade, Ashwin, et al.
Published: (2024)
by: Nalwade, Ashwin, et al.
Published: (2024)
On the Expressive Power of Subgraph Graph Neural Networks for Graphs with Bounded Cycles
by: Chen, Ziang, et al.
Published: (2025)
by: Chen, Ziang, et al.
Published: (2025)
Homomorphism Expressivity of Spectral Invariant Graph Neural Networks
by: Gai, Jingchu, et al.
Published: (2025)
by: Gai, Jingchu, et al.
Published: (2025)
Invariant-Stratified Propagation for Expressive Graph Neural Networks
by: Hevapathige, Asela, et al.
Published: (2026)
by: Hevapathige, Asela, et al.
Published: (2026)
On the Relationship Between Robustness and Expressivity of Graph Neural Networks
by: Kummer, Lorenz, et al.
Published: (2025)
by: Kummer, Lorenz, et al.
Published: (2025)
Full-Spectrum Graph Neural Networks: Expressive and Scalable
by: Wang, Xiaohan, et al.
Published: (2026)
by: Wang, Xiaohan, et al.
Published: (2026)
On the Expressive Power of Geometric Graph Neural Networks
by: Joshi, Chaitanya K., et al.
Published: (2023)
by: Joshi, Chaitanya K., et al.
Published: (2023)
Subsampling Graphs with GNN Performance Guarantees
by: Jain, Mika Sarkin, et al.
Published: (2025)
by: Jain, Mika Sarkin, et al.
Published: (2025)
Random Search Neural Networks for Efficient and Expressive Graph Learning
by: Ito, Michael, et al.
Published: (2025)
by: Ito, Michael, et al.
Published: (2025)
Will More Expressive Graph Neural Networks do Better on Generative Tasks?
by: Zou, Xiandong, et al.
Published: (2023)
by: Zou, Xiandong, et al.
Published: (2023)
GOAt: Explaining Graph Neural Networks via Graph Output Attribution
by: Lu, Shengyao, et al.
Published: (2024)
by: Lu, Shengyao, et al.
Published: (2024)
The Expressive Power of Graph Neural Networks: A Survey
by: Zhang, Bingxu, et al.
Published: (2023)
by: Zhang, Bingxu, et al.
Published: (2023)
Reducing Smoothness with Expressive Memory Enhanced Hierarchical Graph Neural Networks
by: Bailie, Thomas, et al.
Published: (2025)
by: Bailie, Thomas, et al.
Published: (2025)
Boosting Graph Neural Network Expressivity with Learnable Lanczos Constraints
by: Azizi, Niloofar, et al.
Published: (2024)
by: Azizi, Niloofar, et al.
Published: (2024)
G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning
by: Guo, Xiaojun, et al.
Published: (2025)
by: Guo, Xiaojun, et al.
Published: (2025)
Sample Complexity Bounds for Estimating Probability Divergences under Invariances
by: Tahmasebi, Behrooz, et al.
Published: (2023)
by: Tahmasebi, Behrooz, et al.
Published: (2023)
The Exact Sample Complexity Gain from Invariances for Kernel Regression
by: Tahmasebi, Behrooz, et al.
Published: (2023)
by: Tahmasebi, Behrooz, et al.
Published: (2023)
Towards Dynamic Graph Neural Networks with Provably High-Order Expressive Power
by: Wang, Zhe, et al.
Published: (2024)
by: Wang, Zhe, et al.
Published: (2024)
Expressivity of Graph Neural Networks Through the Lens of Adversarial Robustness
by: Campi, Francesco, et al.
Published: (2023)
by: Campi, Francesco, et al.
Published: (2023)
Similar Items
-
Neural Networks With Dense Weights Are Not Universal Approximators
by: Rauchwerger, Levi, et al.
Published: (2026) -
A Note on Graphon-Signal Analysis of Graph Neural Networks
by: Rauchwerger, Levi, et al.
Published: (2025) -
Survey on Generalization Theory for Graph Neural Networks
by: Vasileiou, Antonis, et al.
Published: (2025) -
A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation
by: Amran, Ofek, et al.
Published: (2026) -
PieClam: A Universal Graph Autoencoder Based on Overlapping Inclusive and Exclusive Communities
by: Zilberg, Daniel, et al.
Published: (2024)