Latest Articles for Graph Neural Networks

Research on improving continual learning models through task and class order sensitivity.

Teddy improves GNN performance while reducing computational costs through edge sparsification.

New framework enhances data selection and graph cleaning in noisy environments.

New method improves GNN explainability using proxy graphs.

This work introduces Orderless Regularization to improve graph generation using autoregressive models.

A new model uses AI to better predict COVID-19 variants and their spread.

Using deep reinforcement learning for better data traffic management.

A novel method improves graph generation by considering node and edge attributes together.

A new framework combines DDD and machine learning to better study metal deformation.

This study improves GNN performance through effective graph explanations.

Moco uses machine learning to enhance combinatorial optimization problem solutions.

RC-GNN enhances GNN interpretability and predictive accuracy through innovative methods.

A new method offers clearer insights into GNN predictions without extra training.

A2GNN model enhances performance in adapting graph knowledge across different domains.

A new framework improves the clarity and scalability of graph visualizations.

New methods improve charged particle tracking in high-energy physics experiments.

Graph neural networks help predict roller bearing behavior, improving monitoring and maintenance.

Introducing a new model for predicting connections in various graph types.

A new tool for improving fluid mechanics research through Lagrangian methods.

Graph Mamba Networks offer a new approach for efficient graph analysis.

A new approach improves link prediction accuracy using a mixture of expert models.

Introducing a novel approach to identify misinformation in the digital age.

Learn how LinkedIn uses Graph Neural Networks for better user recommendations.

New models enhance understanding and prediction of surfactant properties.

A new method improves graph neural networks' performance on unseen data.

A new framework enhances job matching efficiency on LinkedIn.

BuffGraph improves classification for less common classes in imbalanced graph data.

Hyperedge Augmentation enhances GNNs by capturing complex relationships in data.

A new approach enhances predictions of material properties using self-supervised learning.

Assessing GNN effectiveness against security risks in integrated circuits.

PhenoLinker enhances the prediction of gene-phenotype associations using advanced AI techniques.

Benchmark study assesses GNN performance on out-of-distribution materials.

A new framework enhances GNN performance using Processing-In-Memory systems.

Distributional Edge Layouts improve GNN performance by sampling diverse graph structures.

A new method improves analysis of granular flows using machine learning techniques.

A look at deep learning methods in drug discovery.

A new approach enhances product search relevance in e-commerce using advanced models.

New method improves graph data analysis through nonlinear sheaf diffusion.

Introducing a method to improve predictions in changing graph data environments.

Examining how graph neural networks predict unseen data effectively.