Latest Articles for Graph Neural Networks

How counterfactual explanations aid molecular behavior prediction and its implications.

Researchers enhance GNNs for better understanding of complex graph structures.

A novel approach to study gut microbiome relationships for predicting health conditions.

A new framework that enhances MLP performance in graph classification using GNN knowledge.

A new approach to enhancing Graph Neural Networks through complex vertex representation.

HydraGNN uses machine learning to accelerate materials discovery and modeling.

Researchers enhance material property prediction accuracy using transfer learning and machine learning techniques.

GFN-ROM enhances model order reduction using graph neural networks for efficient simulations.

Innovative approach improves satellite fault detection without extensive labeled data.

A new pooling operator using Ricci curvature enhances graph learning.

GNN and RL technology enhance efficiency and adaptability in power grid operations.

A novel graph-based method improves multi-object tracking accuracy and efficiency.

T-Hop framework leverages path information for better predictions in drug discovery.

A new method enhances the evaluation of SQL code generation accuracy.

A new method uses deep learning for improved monitoring of structures.

GRASS improves graph neural networks with innovative rewiring and attention mechanisms.

A look into the challenges and innovations in graph domain adaptation methods.

Discover how machine learning aids in top quark analysis.

Innovative method improves clarity in music scores for piano performance.

Researchers simplify methods for processing text and graphs using language models.

Integrating causal knowledge improves Graph Neural Networks performance in diverse tasks.

This article explores methods for explaining node similarities in graph data.

A novel method enhances edge weight predictions using Graph Neural Networks.

A look into the advancements of GNNs and their interpretability.

GraphMuse streamlines the analysis of symbolic music data with advanced machine learning techniques.

EQuAD improves model performance on graphs facing changing data distributions.

Learn about graph transformers and their applications in various fields.

A new approach enhances predictions for new items using unique interaction patterns.

This article discusses safeguarding GNNs from data poisoning and backdoor attacks.

New techniques improve predictions of defect formation energies in materials.

Machine learning enhances prediction of activity coefficients in chemical mixtures.

Establishing a benchmark to evaluate fairness in graph learning methods.

A study on the resilience of Graph Transformers against adversarial attacks.

Discover how GDAN enhances credit risk analysis using advanced graph techniques.

New frameworks for efficient modeling of complex networked systems in electrified vehicles.

SafePowerGraph enhances GNN performance in power grid operations prioritizing safety.

A new approach enhances graph neural networks using large language models.

A new method predicts product sales during busy events using proxy data.

Using GNNs to predict optical properties of materials improves device design.

A new technique enhances scene classification using hybrid graph neural networks.