Imagen de Portada

Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Southern, Joshua, Eitan, Yam, Bar-Shalom, Guy, Bronstein, Michael, Maron, Haggai, Frasca, Fabrizio
Formato:	Preprint
Publicado:	2025
Materias:	Machine Learning Neural and Evolutionary Computing
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

A Flexible, Equivariant Framework for Subgraph GNNs via Graph Products and Graph Coarsening
por: Bar-Shalom, Guy, et al.
Publicado: (2024)

Towards Foundation Models on Graphs: An Analysis on Cross-Dataset Transfer of Pretrained GNNs
por: Frasca, Fabrizio, et al.
Publicado: (2024)

Topological Blindspots: Understanding and Extending Topological Deep Learning Through the Lens of Expressivity
por: Eitan, Yam, et al.
Publicado: (2024)

On The Expressive Power of GNN Derivatives
por: Eitan, Yam, et al.
Publicado: (2025)

FS-KAN: Permutation Equivariant Kolmogorov-Arnold Networks via Function Sharing
por: Elbaz, Ran, et al.
Publicado: (2025)

Subgraphormer: Unifying Subgraph GNNs and Graph Transformers via Graph Products
por: Bar-Shalom, Guy, et al.
Publicado: (2024)

Future Directions in the Theory of Graph Machine Learning
por: Morris, Christopher, et al.
Publicado: (2024)

Neural Message-Passing on Attention Graphs for Hallucination Detection
por: Frasca, Fabrizio, et al.
Publicado: (2025)

RsGCN: Subgraph-Based Rescaling Enhances Generalization of GCNs for Solving Traveling Salesman Problems
por: Huang, Junquan, et al.
Publicado: (2025)

Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks
por: Bechler-Speicher, Maya, et al.
Publicado: (2025)

On the Expressive Power of Permutation-Equivariant Weight-Space Networks
por: Dayan, Adir, et al.
Publicado: (2026)

Beyond Token Probes: Hallucination Detection via Activation Tensors with ACT-ViT
por: Bar-Shalom, Guy, et al.
Publicado: (2025)

Walking the Weight Manifold: a Topological Approach to Conditioning Inspired by Neuromodulation
por: Benjamin, Ari S., et al.
Publicado: (2025)

Greener GRASS: Enhancing GNNs with Encoding, Rewiring, and Attention
por: Liao, Tongzhou, et al.
Publicado: (2024)

Balanced Resonate-and-Fire Neurons
por: Higuchi, Saya, et al.
Publicado: (2024)

Understanding the Convergence in Balanced Resonate-and-Fire Neurons
por: Higuchi, Saya, et al.
Publicado: (2024)

From Tempered to Benign Overfitting in ReLU Neural Networks
por: Kornowski, Guy, et al.
Publicado: (2023)

GraphBench: Next-generation graph learning benchmarking
por: Stoll, Timo, et al.
Publicado: (2025)

Skewed Neuronal Heterogeneity Enhances Efficiency On Various Computing Systems
por: Golmohammadi, Arash, et al.
Publicado: (2024)

Expressivity of Neural Networks with Random Weights and Learned Biases
por: Williams, Ezekiel, et al.
Publicado: (2024)

Identifying Easy Instances to Improve Efficiency of ML Pipelines for Algorithm-Selection
por: Renau, Quentin, et al.
Publicado: (2024)

Training a General Spiking Neural Network with Improved Efficiency and Minimum Latency
por: Yao, Yunpeng, et al.
Publicado: (2024)

Advancing Training Efficiency of Deep Spiking Neural Networks through Rate-based Backpropagation
por: Yu, Chengting, et al.
Publicado: (2024)

Synaptic Modulation using Interspike Intervals Increases Energy Efficiency of Spiking Neural Networks
por: Adams, Dylan, et al.
Publicado: (2024)

Drawback of Enforcing Equivariance and its Compensation via the Lens of Expressive Power
por: Chen, Yuzhu, et al.
Publicado: (2025)

Boosting Brain-inspired Path Integration Efficiency via Learning-based Replication of Continuous Attractor Neurodynamics
por: Ge, Zhangyu, et al.
Publicado: (2025)

LeanKAN: A Parameter-Lean Kolmogorov-Arnold Network Layer with Improved Memory Efficiency and Convergence Behavior
por: Koenig, Benjamin C., et al.
Publicado: (2025)

Expressivity and Approximation Properties of Deep Neural Networks with ReLU$^k$ Activation
por: He, Juncai, et al.
Publicado: (2023)

Probabilistically Rewired Message-Passing Neural Networks
por: Qian, Chendi, et al.
Publicado: (2023)

Better Neural Network Expressivity: Subdividing the Simplex
por: Bakaev, Egor, et al.
Publicado: (2025)

On The Expressivity of Recurrent Neural Cascades
por: Knorozova, Nadezda Alexandrovna, et al.
Publicado: (2023)

SHIRE: Enhancing Sample Efficiency using Human Intuition in REinforcement Learning
por: Joshi, Amogh, et al.
Publicado: (2024)

Deep Learning-Based Operators for Evolutionary Algorithms
por: Shem-Tov, Eliad, et al.
Publicado: (2024)

Switch-Based Multi-Part Neural Network
por: Majumder, Surajit, et al.
Publicado: (2025)

Experience-Based Evolutionary Algorithms for Expensive Optimization
por: Yu, Xunzhao, et al.
Publicado: (2023)

Enabling Population-Based Architectures for Neural Combinatorial Optimization
por: Garmendia, Andoni Irazusta, et al.
Publicado: (2026)

Genetic Programming with Transformer-Based Mutation for Approximate Circuit Design
por: Galeta, Ondrej, et al.
Publicado: (2026)

Migrating Birds Optimization-Based Feature Selection for Text Classification
por: Kaya, Cem, et al.
Publicado: (2024)

Trust Region-Based Bayesian Optimisation to Discover Diverse Solutions
por: Perera, Kokila Kasuni, et al.
Publicado: (2025)

Generalized Dynamic Brain Functional Connectivity Based on Random Convolutions
por: Duan, Yongjie, et al.
Publicado: (2024)