Latest Articles for Graph Neural Networks

Improving software security by detecting vulnerabilities before exploitation.

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

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

Examining scaling strategies to enhance GNN performance in molecular graph tasks.

A new approach improves graph filter performance across various data types.

Graph4GUI improves user interface design through enhanced representation and management.

VN-EGNN improves drug development by enhancing binding site identification on proteins.

This paper presents a graph-based method for enhancing patent claim approval predictions.

New methods improve application image placement in edge computing for faster user access.

NeuraChip improves efficiency in processing graph data for various applications.

MiniMol offers an efficient approach to predicting molecular properties with fewer parameters.

NuGraph2 enhances the study of neutrino interactions using advanced graph neural networks.

Crystalformer efficiently predicts material properties using advanced attention mechanisms.

Counterfactual reasoning enhances understanding of vulnerabilities in code.

New method combines phylogenetic tree and network for improved analysis.

FROND utilizes fractional calculus to enhance Graph Neural Networks for complex data.

New techniques enhance our understanding of dislocation behavior in materials.

New approach reveals how AI predicts material properties transparently.

New method improves explanations for graph neural networks with robust counterfactual witnesses.

A new model redefines graph analysis using point sets for improved predictions.

New methods in deep learning for accurate predictions of nuclear cross sections.

A new model enhances image retrieval for breast histopathology diagnosis.

Learn how graph analysis helps detect unusual patterns in data.

Using GNNs to identify multifunctional proteins enhances biological research.

New methods enhance realistic clothing behavior in animations.

A new model improves missing data handling in time series analysis.

A study on the strengths and weaknesses of two graph processing models.

GNN-Diff improves GNN training efficiency and performance by optimizing hyperparameters.

Introducing a novel approach to learning about geographical areas and their connections.

Virtual nodes help improve performance in graph neural networks by enhancing information flow.

A new method improves understanding of Graph Neural Networks' predictions.

A method that customizes graph sparsification for better neural network performance.

A new method for improving graph data analysis and communication.

ETNNs enhance complex data analysis through topological and geometric integration.

drGAT uses machine learning to predict how cells respond to drugs.

A new framework improves GNNs by using clustering to enhance learning.

Automatic feature extraction transforms data analysis at particle colliders.

Exploring the role of GNNs in the growth of IoT technologies.

DSAM offers a fresh perspective on brain connectivity using advanced deep learning techniques.

DeepHGNN enhances hierarchical forecasting accuracy using graph neural networks.