Latest Articles for Graph Neural Networks

New neural networks improve analysis of complex, directed data relationships.

New method improves detection of Parkinson's Disease through speech analysis with advanced technology.

CrossGR enhances product recommendations across markets using innovative graph technology.

A new framework improves atom representations, enhancing predictions in material science.

New machine learning techniques improve predictions in polycrystal plasticity.

New methods enhance GNNs for challenging graph types.

GenMS transforms material research by generating new crystal structures from simple language inputs.

New AI techniques improve fluid dynamics modeling accuracy and efficiency.

Introducing a pooling operator that maintains data integrity while improving computation speed.

A look into how gene models change medical treatments for individual patients.

New methods improve high-level synthesis through efficient design space exploration.

GIFFLAR improves glycan property predictions using graph neural networks.

New techniques improve early prediction of chronic health issues using patient data.

A new method enhances the resilience of GNNs against adversarial attacks.

Study of machine-learning models predicting properties in solid materials, focusing on defects.

New methods enhance object location tracking in dense urban areas.

Combining optimal transport and structural models improves single-cell data interpretation.

Combining graph neural networks and variational autoencoders enhances image classification accuracy.

A new method integrates graph neural networks for enhanced molecular simulations.

Research uses Graph Neural Networks to predict force chains in dense suspensions.

TITAN innovates traffic flow predictions using expert models for better accuracy.

A new framework enhances accuracy in atomistic simulations through advanced force field optimization.

GraphLoRA enhances the transferability of Graph Neural Networks across varied graphs.

New approaches improve modeling of physical behaviors in varying geometries.

TwinCL improves recommendation performance through innovative graph techniques.

A new method simplifies feature selection in molecular simulations using graph neural networks.

This paper enhances federated learning for GNNs while preserving user privacy.

AUGUR simplifies finding optimal spots for molecular attachment using advanced techniques.

A fresh approach to understanding crime in cities using location and landmarks.

Learn how GNNs enhance Remaining Useful Life predictions for industrial equipment.

Learn how SMPNNs manage complex data connections effectively.

Researchers use simulations to study galaxy interactions and intrinsic alignments.

Exploring the merge of communication and sensing for advanced wireless networks.

New methods shed light on GNNs and their properties.

Research reveals risks of property inference attacks on GNNs and the need for data privacy.

GNNs are reshaping how we analyze and understand Earth data.

Exploring new ways to combat money laundering through data sharing and machine learning.

TIMBRE offers a new way to match candidates with jobs effectively.

This article discusses fairness issues in Graph Neural Networks and introduces a new method.

Learn how surfactants and GNNs improve cleaning product development.