graph neural networks

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

This paper proposes ForecastClickGraph, a deep learning framework that extracts cross-product relationships and demand information from clickstream data for probabilistic sales forecasting. ForecastClickGraph models stages in consumer shopping journeys as a dynamic graph, where nodes represent individual product-stag...

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 key step in the biological interpretation of untargeted metabolomics data is the ranking of compounds by importance, usually using statistical significance or impact on classification as the basis for importance assignment. However, current approaches treat metabolites as independent variables and rarely incorporat...

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

Measuring AI Progress in Drug Discovery: A Reproducible Leaderboard for the Tox21 Challenge

Abstract Deep learning has fundamentally transformed fields such as computer vision and natural language processing since the early 2010s and has strongly influenced biomedical research. For drug discovery specifically, a key inflection point─akin to vision’s “ImageNet moment”─arrived in 2015, when deep neural networks...

A Multi-Resource Coordinated Scheduling Optimization Framework Integrating Berth Allocation, Quay Crane Operation, and AGV Transportation in Automated Container Terminals

This study investigates the operations in automated container terminals, in which scheduling decisions need to be generated automatically. Therefore, an integrated optimization model is established considering the two-way transportation process of containers between the berth and the yard with the assignment and schedu...