Latest Articles for Graph Neural Networks

Introducing Directional Graph Attention Network for improved node learning in complex graphs.

A new framework enhances graph pre-training by selecting and weighing tasks effectively.

A new method uses machine learning to detect errors in MPI programs effectively.

Robots can navigate better by actively seeking information about their surroundings.

A new model efficiently targets nodes in GNNs with minimal changes.

A new method enhances molecule-caption translation using Large Language Models.

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

A new framework improves item suggestions by balancing social influence and user preferences.

AnisoGNN uses AI to predict polycrystalline material properties with less data.

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

This study explores how topology awareness affects Graph Neural Network performance and fairness.

A new method enhances GNNs by improving graph structure recognition.

Exploring graph homomorphism to enhance GNN generalization capabilities.

Understanding how Graph Neural Networks model complex relationships in data.

A new method improves GNN performance by utilizing explanation subgraphs.

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

Study reveals factors affecting single-layer GCN's performance on various data models.

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

A new method enhances the learning capabilities of Graph Neural Networks.

E-SHARC improves speaker identification in various audio environments.

A new method improves fluid dynamics simulations using neural networks.

Exploring new models for better graph data analysis.

Examining the role of deep learning in medical image analysis and adversarial threats.

Innovative methods improve 3D facial expressions for realistic digital characters.

A new protocol improves the training efficiency of Graph Neural Networks using CPU and GPU.

CANOS predicts near-optimal power flow solutions rapidly for complex power grids.

A new method improves recommendations using adaptable embeddings and efficient resource management.

A new AI method improves scheduling in uncertain task environments.

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

Examining GNNs for classifying particle collision events.

A new model transforms retrosynthesis predictions using graph neural networks.

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

Introducing Smart, a method for better GNN performance in changing graphs.

Using deep learning to improve public transit efficiency and reduce costs.

A new method enhances GNSS location accuracy in cities with tall buildings.

A new method using deep learning tackles cascading failures in complex networks.

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

A fresh approach to estimating causal effects in social networks.

New methods improve anomaly detection in Network Intrusion Detection Systems.

Improving software security by detecting vulnerabilities before exploitation.