graph neural networks

Towards Efficiency and Accuracy: Learning One Compact View for Multiplex Graph Neural Networks via Structure-Aware Augmentation

Graph neural networks in the Wilson loop representation of Abelian lattice gauge theories

AI Visualization for Analyzing Factors Influencing Tourists' Satisfaction Under Deep Learning Technology

To analyze Chinese tourist satisfaction, this study proposed an advanced multimodal feature fusion deep learning framework integrating a robustly optimized bidirectional encoder representations from transformers pretraining approach (RoBERTa), a convolutional neural network (CNN), and a graph neural network (GNN). The...

EXPRESS: Let Clickstream Talk: A Graph Neural Network Approach to Sales Forecasting

A deep learning framework that extracts cross-product relationships and demand information from clickstream data for probabilistic sales forecasting that outperforms state-of-the-art benchmark models by 10-28% in forecast accuracy and yields superior probabilistic forecasts with substantially lower quantile loss and im...

Regional Marine Gravity Field Refinement by Integrating Region-Adaptive Fusion and Multi-Relational Graph Residual Learning

Satellite-altimetry-derived marine gravity models often exhibit limited regional adaptability and region-dependent residual errors relative to shipborne observations, particularly in coastal, shelf, and slope areas. This study proposes a regional marine gravity refinement method that integrates region-adaptive backgrou...

How Brains Close Loops and Why Your GNN Should Too: A Cognitively Motivated Topological Extension of Message Passing on Graphs

Enhancing interpretability in metabolomics: ranking metabolites by their impact on graph neural network predictions

A new method where Formula Difference Networks are used as inputs to predictive Graph Neural Network models, and it is found that there was a clear bias for assigning higher importance to nodes in connected subgraphs.

Graph Learning for River Water Quality Forecasting: Advances in Hydrological Connectivity Representation, Dynamic Topology, and Physics-Informed Modeling

Graph neural networks are increasingly used for river water-quality forecasting because they can represent interactions among monitoring locations that conventional site-wise time-series models cannot capture. However, the usefulness of graph learning depends strongly on whether graph structure reflects the hydrologica...

A physics-guided graph neural network framework for prediction of offshore wind farm power and fatigue loads

Tensor Channel Equivariant Graph Neural Networks for Molecular Polarizability Prediction

Revealing the Pitfalls and Re-evaluating the Advancement of Heterophilic Graph Learning

Geometric Self-supervised Pretraining on 3D Protein Structures Using Subgraphs