Latest Articles for GNNs

A new approach for clearer GNN predictions using edge-focused subgraph explanations.

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

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

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

Link2Doc enhances link prediction by merging text and graph structure.

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

This approach improves function comparison in software through call graphlets.

This paper presents a new approach to enhance KGQA performance using GNNs and LLMs.

A new approach to boost GNNs against noise and attacks.

Exploring the challenges of GNN explainers under adversarial attacks in critical applications.

A novel approach to integrate transformers with graph structures for better outcomes.

A new benchmark helps improve GNN performance amid label noise challenges.

This article examines challenges in link prediction due to data distribution shifts.

A new measure, GGD, improves how we assess similarities and differences in graphs.

A new dual-transformer model enhances execution time predictions from source code analysis.

A new perspective on enhancing GNNs for complex graph structures.

AMBER leverages machine learning to automate mesh generation for engineering simulations.

A new technique enhances graph neural networks performance on large datasets.

Commute Graph Neural Networks improve understanding of directed graphs through enhanced relationship insights.

ITEM framework improves training and evaluation of GNNs for better recommendations.

GNN and RL technology enhance efficiency and adaptability in power grid operations.

Multi-Peptide combines data models to enhance peptide behavior predictions.

GraphMuse streamlines the analysis of symbolic music data with advanced machine learning techniques.

Learn about graph transformers and their applications in various fields.

Discover how Routing-by-Memory enhances MLP performance in graph neural networks.

DeepGate3 enhances circuit design understanding and scalability with innovative model architecture.

A new approach enhances predictions for new items using unique interaction patterns.

Using machine learning to enhance the interpretability of molecular simulations.

A novel method enhances GNN performance with noisy data.

Establishing a benchmark to evaluate fairness in graph learning methods.

New methods enhance uncertainty estimates in GNNs for modeling material energies.

A new approach improves GNN reasoning capabilities for complex relationship tasks.

scTransNet framework improves understanding of gene interactions using single-cell RNA data.

Wav2graph creates knowledge graphs from spoken language for improved AI understanding.

A new method aims to enhance recommendations in changing data environments.

New GNN models enhance molecular representation for better predictions in science.

This article discusses a new method for classifying graphs using basic structural features.

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

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

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