Cover Image

A note on the VC dimension of 1-dimensional GNNs

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Daniëls, Noah, Geerts, Floris
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

From Features to Structure: Task-Aware Graph Construction for Relational and Tabular Learning with GNNs
by: Cucumides, Tamara, et al.
Published: (2025)

Grables: Tabular Learning Beyond Independent Rows
by: Cucumides, Tamara, et al.
Published: (2026)

Towards Bridging Generalization and Expressivity of Graph Neural Networks
by: Li, Shouheng, et al.
Published: (2024)

Weisfeiler-Leman at the margin: When more expressivity matters
by: Franks, Billy J., et al.
Published: (2024)

A Logical View of GNN-Style Computation and the Role of Activation Functions
by: Barceló, Pablo, et al.
Published: (2025)

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

VC dimension of Graph Neural Networks with Pfaffian activation functions
by: D'Inverno, Giuseppe Alessio, et al.
Published: (2024)

On the VC dimension of deep group convolutional neural networks
by: Sepliarskaia, Anna, et al.
Published: (2024)

A Neuro-Symbolic Framework for Answering Graph Pattern Queries in Knowledge Graphs
by: Cucumides, Tamara, et al.
Published: (2023)

Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification
by: Luo, Yuankai, et al.
Published: (2024)

On rough mereology and VC-dimension in treatment of decision prediction for open world decision systems
by: Polkowski, Lech T.
Published: (2024)

Towards Precise Prediction Uncertainty in GNNs: Refining GNNs with Topology-grouping Strategy
by: Seo, Hyunjin, et al.
Published: (2024)

On the Scalability of GNNs for Molecular Graphs
by: Sypetkowski, Maciej, et al.
Published: (2024)

Locality-Aware Graph-Rewiring in GNNs
by: Barbero, Federico, et al.
Published: (2023)

Universally Invariant Learning in Equivariant GNNs
by: Cen, Jiacheng, et al.
Published: (2025)

Generalizing GNNs with Tokenized Mixture of Experts
by: Guo, Xiaoguang, et al.
Published: (2026)

Are GNNs doomed by the topology of their input graph?
by: Aboussalah, Amine Mohamed, et al.
Published: (2025)

Chordless Structure: A Pathway to Simple and Expressive GNNs
by: Pan, Hongxu, et al.
Published: (2025)

Acceleration Algorithms in GNNs: A Survey
by: Ma, Lu, et al.
Published: (2024)

VC Theory for Inventory Policies
by: Xie, Yaqi, et al.
Published: (2024)

Which Algorithms Can Graph Neural Networks Learn?
by: Wittig, Solveig, et al.
Published: (2026)

Expressivity and Generalization: Fragment-Biases for Molecular GNNs
by: Wollschläger, Tom, et al.
Published: (2024)

On the Expressive Power of GNNs to Solve Linear SDPs
by: Qian, Chendi, et al.
Published: (2026)

On the Limitation and Experience Replay for GNNs in Continual Learning
by: Su, Junwei, et al.
Published: (2023)

'Hello, World!': Making GNNs Talk with LLMs
by: Kim, Sunwoo, et al.
Published: (2025)

Fixed Aggregation Features Can Rival GNNs
by: Rubio-Madrigal, Celia, et al.
Published: (2026)

Understanding GNNs and Homophily in Dynamic Node Classification
by: Ito, Michael, et al.
Published: (2025)

A Signed Graph Approach to Understanding and Mitigating Oversmoothing in GNNs
by: Wang, Jiaqi, et al.
Published: (2025)

Chi-Geometry: A Library for Benchmarking Chirality Prediction of GNNs
by: Weaver, Rylie, et al.
Published: (2025)

SympGNNs: Symplectic Graph Neural Networks for identifiying high-dimensional Hamiltonian systems and node classification
by: Varghese, Alan John, et al.
Published: (2024)

Graph Classification with GNNs: Optimisation, Representation and Inductive Bias
by: a, P. Krishna Kumar, et al.
Published: (2024)

Understanding GNNs for Boolean Satisfiability through Approximation Algorithms
by: Hůla, Jan, et al.
Published: (2024)

Improving Molecular Modeling with Geometric GNNs: an Empirical Study
by: Ramlaoui, Ali, et al.
Published: (2024)

Simple GNNs with Low Rank Non-parametric Aggregators
by: Vinas, Luciano, et al.
Published: (2023)

Flood and Echo Net: Algorithmically Aligned GNNs that Generalize
by: Mathys, Joël, et al.
Published: (2023)

Chaining 2-FWL GNNs for Combinatorial Graph Alignment
by: Lelarge, Marc
Published: (2025)

Geometric GNNs for Charged Particle Tracking at GlueX
by: Mohammed, Ahmed Hossam, et al.
Published: (2025)

Flow Matters: Directional and Expressive GNNs for Heterophilic Graphs
by: Gupta, Arman, et al.
Published: (2025)

Rethinking the Expressive Power of GNNs via Graph Biconnectivity
by: Zhang, Bohang, et al.
Published: (2023)

Adaptive Initial Residual Connections for GNNs with Theoretical Guarantees
by: Shirzadi, Mohammad, et al.
Published: (2025)