Latest Articles for Graph Neural Networks

EggNet enhances particle tracking accuracy using evolving graph-based techniques.

A new GNN method offers faster evaluation of traffic policies for cities.

A new method improves GNN architecture design using insights from language models.

Introducing SimpleSBDD, a method streamlining drug discovery by optimizing binding affinity.

Exploring the explainability issues in Graph Neural Networks and potential solutions.

QGNNs offer innovative ways to model and predict molecular properties using quantum mechanics.

GrassNet uses State Space Models for adaptive filtering in graph neural networks.

A new method addresses challenges in Graph Neural Networks with heterophilic data.

Learn how Graph Neural Networks improve quantum error correction techniques.

AI methods are changing how scientists predict molecular properties for various applications.

A new approach enhances graph clustering by combining structural and attribute information.

An overview of the merging of quantum computing with graph neural networks.

Innovative framework improves environmental impact analysis of electronic components.

New method improves satellite image scheduling using advanced machine learning techniques.

A new method improves link prediction in graph neural networks using LLwLC.

Exploring fairness in personalized recommendations using graph neural networks.

A new method helps GNNs learn from unfamiliar data effectively.

A novel method to enhance data discovery and analysis for better predictions.

InfoIGL enhances graph neural networks' performance in varied data environments.

Combining flow and packet data revolutionizes network threat detection.

This article examines how GNNs enhance the solving of SAT problems.

A study on improving efficiency in network slice management across multiple domains.

A new method enhances the accuracy of predicting hospital readmissions using diverse patient data.

Exploring local symmetries to enhance graph-based machine learning methods.

Examining how deep learning improves earthquake prediction accuracy.

A new method improves evolutionary tree construction using deep learning.

A new method for reducing complex graphs while retaining key features for classification.

TG-PhyNN combines neural networks and physical laws for better predictions.

GRLinQ enhances direct device communication using machine learning techniques.

Using GNNs to improve accuracy in electricity consumption forecasting.

A new method enhances understanding of cancer drug responses through clearer explanations.

A new approach to transmit graph data effectively by focusing on essential information.

Improving cybersecurity through enhanced anomaly detection using GNNs.

Learn how GNNs can better generalize to unseen data.

This research presents a method to predict stock market volatility accurately.

New method enhances analysis of complex systems through Symplectic Graph Neural Networks.

Exploring the role of non-autoregressive networks in solving complex optimization problems.

Introducing GSSC, a framework to improve graph learning without traditional message passing.

Meta Flow Matching enhances our understanding of cell behavior and treatment responses.

PharmacoMatch uses machine learning to enhance pharmacophore screening efficiency.