Latest Articles for GNNs

New methods enhance speed and efficiency in analyzing particle collision data.

New hardware system enhances GNN inference speed and efficiency for real-time applications.

This article discusses methods for improving knowledge graph completion using GNNs and rule-based approaches.

This method improves the classification of medical images using graph neural networks.

This study uses GNNs to analyze bike-sharing data for better predictions of usage patterns.

HQ-GNN enhances recommendations while reducing memory and increasing speed.

This study evaluates advanced GNNs for generating molecular graphs more effectively.

This study examines deep learning models for forecasting visitor numbers in tourism.

A new GNN approach improves accuracy by reducing over-smoothing.

Explore how GNNs and Knowledge Graphs enhance healthcare link predictions.

A novel approach to classifying research articles using advanced graph techniques.

A new evaluation method enhances understanding of GNN predictions.

A new model improves predictions for electron density in catalysis research.

A new model tackles challenges in graph neural networks using power series.

A new model turns cooking recipes into graphs for easier understanding.

Examining fairness challenges in GNNs amidst changing data distributions.

ARGO improves GNN training speed and efficiency on multi-core systems.

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

New method improves the discovery of crystal materials for technology and sustainability.

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

Hyperedge Augmentation enhances GNNs by capturing complex relationships in data.

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

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

Examining how graph neural networks predict unseen data effectively.

K-Link enhances MTS data analysis through improved sensor interaction understanding.

A fast and efficient method for graph processing without complex models.

A look at how GNNs analyze graphs and adapt to changes.

A new method improves GNN performance by utilizing explanation subgraphs.

A method to assess the impact of user removal on network opinions.

A new method to improve model training from noisy graph data.

Introducing innovative GNN layers to analyze complex data on manifolds.

A new method improves fluid dynamics simulations using neural networks.

PowerGraph enhances analysis of power grid failures through advanced learning techniques.

Research investigates how network structure relates to stability using advanced methods.

A new method, DepGraph, improves software bug detection accuracy.

A new method accelerates merge tree comparisons in scientific data analysis.

A new model improves predictions for surfactant performance based on temperature.

A new approach using graph neural networks for efficient multiscale material simulations.

A new approach using RGCNs improves sentiment classification from text data.

Introducing TrainlessGNN for faster node labeling in TAGs without traditional training methods.