Cover Image

Leveraging Classical Algorithms for Graph Neural Networks

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Wu, Jason, Veličković, Petar
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Recurrent Aggregators in Neural Algorithmic Reasoning
by: Xu, Kaijia, et al.
Published: (2024)

Parallel Algorithms Align with Neural Execution
by: Engelmayer, Valerie, et al.
Published: (2023)

Latent Space Representations of Neural Algorithmic Reasoners
by: Mirjanić, Vladimir V., et al.
Published: (2023)

NAR-*ICP: Neural Execution of Classical ICP-based Pointcloud Registration Algorithms
by: Panagiotaki, Efimia, et al.
Published: (2024)

KNARsack: Teaching Neural Algorithmic Reasoners to Solve Pseudo-Polynomial Problems
by: Požgaj, Stjepan, et al.
Published: (2025)

Commute-Time-Optimised Graphs for GNNs
by: Sterner, Igor, et al.
Published: (2024)

Cayley Graph Propagation
by: Wilson, JJ, et al.
Published: (2024)

Temporal Graph Rewiring with Expander Graphs
by: Petrović, Katarina, et al.
Published: (2024)

Transformers meet Neural Algorithmic Reasoners
by: Bounsi, Wilfried, et al.
Published: (2024)

Asynchronous Algorithmic Alignment with Cocycles
by: Dudzik, Andrew, et al.
Published: (2023)

Positional Attention: Expressivity and Learnability of Algorithmic Computation
by: de Luca, Artur Back, et al.
Published: (2024)

Optimizers Qualitatively Alter Solutions And We Should Leverage This
by: Pascanu, Razvan, et al.
Published: (2025)

How LLMs Detect and Correct Their Own Errors: The Role of Internal Confidence Signals
by: Kumaran, Dharshan, et al.
Published: (2026)

Causal Evidence that Language Models use Confidence to Drive Behavior
by: Kumaran, Dharshan, et al.
Published: (2026)

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks
by: Su, Junwei, et al.
Published: (2025)

Softmax is not Enough (for Sharp Size Generalisation)
by: Veličković, Petar, et al.
Published: (2024)

Round and Round We Go! What makes Rotary Positional Encodings useful?
by: Barbero, Federico, et al.
Published: (2024)

Reasoning Algorithmically in Graph Neural Networks
by: Numeroso, Danilo
Published: (2024)

Choosing a Classical Planner with Graph Neural Networks
by: Vatter, Jana, et al.
Published: (2024)

Perplexity Cannot Always Tell Right from Wrong
by: Veličković, Petar, et al.
Published: (2026)

Leveraging Graph Neural Networks to Forecast Electricity Consumption
by: Campagne, Eloi, et al.
Published: (2024)

Leveraging Spatiotemporal Graph Neural Networks for Multi-Store Sales Forecasting
by: Singh, Manish, et al.
Published: (2025)

Perseus: Leveraging Common Data Patterns with Curriculum Learning for More Robust Graph Neural Networks
by: Xia, Kaiwen, et al.
Published: (2024)

What makes a good feedforward computational graph?
by: Vitvitskyi, Alex, et al.
Published: (2025)

Filter Equivariant Functions: A symmetric account of length-general extrapolation on lists
by: Lewis, Owen, et al.
Published: (2025)

Mining Generalizable Activation Functions
by: Vitvitskyi, Alex, et al.
Published: (2026)

The Illusion of Stochasticity in LLMs
by: Gu, Xiangming, et al.
Published: (2026)

Rotary Position Encodings for Graphs
by: Reid, Isaac, et al.
Published: (2025)

On Emergences of Non-Classical Statistical Characteristics in Classical Neural Networks
by: Zhao, Hanyu, et al.
Published: (2026)

Learning to Execute Graph Algorithms Exactly with Graph Neural Networks
by: Qharabagh, Muhammad Fetrat, et al.
Published: (2026)

Leveraging Compact Satellite Embeddings and Graph Neural Networks for Large-Scale Poverty Mapping
by: Pettersson, Markus B., et al.
Published: (2025)

Leveraging Vulnerabilities in Temporal Graph Neural Networks via Strategic High-Impact Assaults
by: Jeon, Dong Hyun, et al.
Published: (2025)

Winning Lottery Tickets in Neural Networks via a Quantum-Inspired Classical Algorithm
by: Isogai, Natsuto, et al.
Published: (2026)

How do LLMs Compute Verbal Confidence
by: Kumaran, Dharshan, et al.
Published: (2026)

Inference-friendly Graph Compression for Graph Neural Networks
by: Fan, Yangxin, et al.
Published: (2025)

Algorithm-Informed Graph Neural Networks for Leakage Detection and Localization in Water Distribution Networks
by: Zhang, Zepeng, et al.
Published: (2024)

How does over-squashing affect the power of GNNs?
by: Di Giovanni, Francesco, et al.
Published: (2023)

From Text to Graph: Leveraging Graph Neural Networks for Enhanced Explainability in NLP
by: Yáñez-Romero, Fabio, et al.
Published: (2025)

Transferability of Graph Neural Networks using Graphon and Sampling Theories
by: Neuman, A. Martina, et al.
Published: (2023)

Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction
by: Dablander, Markus
Published: (2024)