Latest Articles for Graph Neural Networks

Graph neural networks improve speaker recognition accuracy by analyzing voice sample relationships.

FedGKD enhances federated learning for Graph Neural Networks by optimizing task feature extraction.

MOGAN enhances robot understanding of multi-object interactions for better manipulation.

A new method enhances predictions using a hierarchical grammatical system.

COLA merges contrastive and meta learning for improved node classification in limited data scenarios.

Machine learning enhances the search for new materials, improving stability predictions.

STMask enhances analysis of gene expression through advanced spatial clustering methods.

Machine learning transforms event reconstruction in particle physics, improving accuracy and efficiency.

This article presents a new method for enhancing word embeddings using probabilistic models.

This study showcases the effectiveness of GNNs in analyzing stiffened panels.

Using graph neural networks to objectively evaluate surgical performance.

A new framework combines CodeBERT and GNNs for better vulnerability detection in software.

This method enhances predictions by combining labeled and unlabeled data through latent graphs.

A novel approach to enhance financial fraud detection using Quantum Graph Neural Networks.

Robots learn to stow objects using fewer examples and improved interaction prediction.

DREAM enhances how robots manage tasks and energy in challenging settings.

A new model enhances GNN understanding of node similarities using transitivity.

Introducing SAR-GNN: A new method for effective graph classification.

Researchers use graph neural networks to better predict MS disease activity.

A novel approach provides clear explanations for graph classifications.

A new system improves GNN performance on large graph datasets.

Deep learning techniques improve monitoring and optimization in electric power systems.

Introducing a probabilistic approach to assess GNN explanations for better reliability.

Study reveals backdoor attack risks in GNN link prediction tasks.

A method to improve understanding and security of Graph Neural Networks.

This paper examines how GNNs enhance defenses against cyber attacks throughout their life cycle.

New methods improve adsorption energy prediction for catalysts using machine learning.

Machine learning improves predictions of semiconductor defects for better material performance.

A new framework improves accuracy in node classification with limited labeled data.

A new model that improves graph learning by focusing on node pairs.

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

Examining the complexities and strategies for neural network learning in various data types.

A new framework enhances GNNs to address oversmoothing issues effectively.

DeepRicci enhances graph neural networks by improving structure and node features.

Advancements in deep learning enhance pandemic prediction accuracy through various modeling approaches.

A new approach uses graph neural networks for better anomaly detection.

A new method for explaining Graph Neural Networks enhances transparency and trust.

A new framework aims to ensure equity in GNN predictions.

This study explores how causality enhances Graph Neural Networks' classification tasks.

Graph Multi-Similarity Learning enhances drug discovery through flexible molecular relationships.