Latest Articles for Graph Neural Networks

A study on the effectiveness of GNNs in recommending personalized content.

New methods improve training speed and accuracy of GNNs using quantization.

Espaloma introduces machine learning to improve molecular simulations and drug discovery.

New methods enhance speed and efficiency in analyzing particle collision data.

New method reveals vulnerabilities in Vertical Federated Learning systems using Graph Neural Networks.

New hardware system enhances GNN inference speed and efficiency for real-time applications.

Examining the impact of GNNs on traditional algorithms and their performance challenges.

GraphCC enhances congestion control through real-time optimization and machine learning techniques.

A model links brain activity to walking difficulties in Parkinson's patients.

A novel method for predicting protein functions using descriptive text instead of labels.

New technology improves body fat measurement accuracy using 3D data.

S2PGNN improves the adaptation of GNNs to labeled data.

A study revealing the challenges GNNs face in counting subgraph patterns.

A fresh approach predicts missing brain measurements using advanced machine learning.

A framework to assess GNNs' effectiveness in recommendation systems.

MolGrapher simplifies the extraction of chemical structures from images using a graph-based approach.

A new method for efficient information sharing in networks using cooperative learning agents.

Examining methods to identify political party affiliations using Twitter activities.

This method improves the classification of medical images using graph neural networks.

New model GravNetNorm enhances point cloud analysis in machine learning.

Event cameras transform visual data capture and processing, improving efficiency and performance.

A framework to identify sparse matrix structures using limited data.

ExGNAS improves efficiency and clarity in graph neural network design.

This study uses GNNs to analyze bike-sharing data for better predictions of usage patterns.

This research develops a model to predict gene profiles using tissue images.

Exploring new methods for improved link prediction in graph analysis.

A new method for training GNNs efficiently one layer at a time.

ReOnto combines machine learning and ontologies to enhance relationship extraction in biomedical texts.

A new approach balances privacy and performance in GNN training.

A new method in GNNs uses multiple vectors to improve predictions.

A novel approach improves graph representation learning using non-Euclidean spaces.

Examining how GNNs can improve by modifying irrelevant graph structures.

Leveraging LLMs to make graph data analysis accessible for everyone.

This study examines deep learning models for forecasting visitor numbers in tourism.

A new model enhances influence spread in social networks efficiently.

Introducing a new method for effective graph clustering using relational information.

A model combines graph neural networks and zero-inflation strategies for better traffic predictions.

Introducing a novel approach to analyze complex sensor data over time.

Generative models are transforming how shapes are designed and optimized across industries.

A look into the relationship between Graph Neural Networks and Graph Neural Tangent Kernel.