Latest Articles for Graph Neural Networks

A look at ReLU networks and their role in artificial intelligence.

AI techniques are reshaping how researchers approach Universal Algebra challenges.

This study shows how directed edges enhance GNN performance, especially in heterophilic graphs.

This research enhances node classification using label non-uniformity in graph neural networks.

Graph Neural Networks enhance state estimation speed and accuracy in power systems.

Combining traditional methods and machine learning to improve urban transit networks.

A new framework enhances performance in graph data processing.

Grad-Align+ improves network alignment without needing extra information.

Gelato offers a simpler, effective method for link prediction in networks.

New method enhances attention learning in graph models using causal reasoning.

Improving slum infrastructure using AI to enhance accessibility and reduce costs.

DEGREE enhances the explainability of Graph Neural Networks, improving transparency.

MCGLPPI framework combines CG modeling and machine learning to predict protein interactions efficiently.

A new model enhances semi-supervised classification in GNNs through optimized node-wise diffusion.

Graph U-Net enhances EIT imaging through improved accuracy and speed.

A new method to improve Graph Neural Networks using framelets and p-Laplacian.

A method to learn from the entire structure of relational databases, improving data representation.

Explore the fundamentals and applications of Deep Learning and its geometric variant.

This article discusses how GNNs improve airfoil airflow predictions.

New simulator predicts granular flow behavior using Graph Neural Networks.

We study how explanations improve understanding in graph neural networks.

Union Subgraph Neural Networks enhance graph analysis by capturing local structures.

A new approach enhances GNN training efficiency on large graphs using advanced techniques.

DeepGate2 enhances circuit representation learning for efficient electronic design automation.

Using GNNs to analyze the geometry of plumbed 3-manifolds.

A study on label position bias in GNNs and its impact on performance.

New approaches aim to improve GCN performance and generalization.

Introducing DrugChat, a system to enhance drug discovery through interactive questions and data analysis.

A new approach uses Graph Neural Networks for better user pairing in wireless systems.

A new model enhances predictions of COVID-19 spread in New Zealand.

A new model improves accuracy in predicting traffic conditions.

Combining graph rewriting with GNNs for dynamic data analysis.

A new approach enhances brain network research using Graph Neural Networks.

A new model enhances GNN performance with incomplete graph data.

A new framework enhances the detection of cyber attack paths.

New techniques enhance efficiency and expressiveness in graph analysis.

Examining rewiring methods to enhance graph neural network performance and address key challenges.

A novel approach to enhance security in military wireless networks.

This article examines attribute inference attacks on GNNs and their impact on privacy.

A look into the importance of explainability in GNNs.