graph neural networks

Ground-Based GNSS Atmospheric Remote Sensing for Ultra-Short-Term Wind-Power Forecasting: A Direction-Proxy-Guided Graph-Residual Approach

Ground-based Global Navigation Satellite System (GNSS) stations provide continuous atmospheric remote sensing through electromagnetic propagation delays. Precise point positioning (PPP) yields zenith tropospheric delay (ZTD), and ZTD gradients provide a proxy for off-farm tropospheric structure that is unavailable to s...

Research on Grid Load Optimization Scheduling Algorithm Based on Line Loss Data

In recent times, power grid optimization has incorporated ML and optimization algorithms to improve the power grid and reduce losses. Graph Neural Networks (GNN) are efficient in modelling complex dependencies, while Particle Swarm Optimization (PSO) optimizes line losses and operating costs. However, the application o...

Conformal Prediction and Multi-Ensemble Gradient Boosting for Subtype-Selective Binding Affinity Estimation Across Human Adenosine Receptors

Subtype selectivity across human adenosine GPCRs remains an intractable medicinal chemistry challenge. The four orthosteric binding pockets share over 70% sequence homology across transmembrane helices III, V, VI, and VII. Consequently, standard QSAR models fail prospectively. They suffer from systemic scaffold leakage...

Graph neural network based topology recognition and operational state assessment for complex power-grid automation control

Practical Graph Intelligence 2

Practical Graph Intelligence 2 delivers a comprehensive and application driven exploration of graph-based methods for understanding complex, interconnected data. This book bridges theory and practice by presenting advanced techniques in graph theory, graph neural networks and network analytics, with a strong focus on r...

Datasets and Implementation Code for Resilient Real-Time Metro Rerouting Using Adaptive Bidirectional Heuristics and Graph Neural Networks

This repository contains the dataset, source codes, trained graph models, and experimental outputs used in the research article entitled: "Resilient Real-Time Metro Rerouting with Adaptive Bidirectional Heuristics and Graph Neural Network-Based Edge Prediction" The provided materials support the implementation and eval...

Automated Product Carbon Footprint Estimation via Low-Latency Neural Language Processing in Multilingual, Real-Time Supply Chains.

Real-time product carbon footprint (PCF) reporting is increasingly required in global supply chains, yet conventional life cycle assessment (LCA) remains difficult to scale due to fragmented multitier data, heterogeneous formats, and substantial manual effort. This article proposes Auto-LCA, a system-level architecture...

A Reliability-Aware Cross-Branch Contrastive Graph Convolutional Network for Hyperspectral Image Processing

Hyperspectral images contain abundant spectral information and provide fine-grained spatial representations. However, their high dimensionality, severe spectral redundancy, subtle inter-class differences, mixed boundary regions, and limited labeled samples pose significant challenges to accurate classification. Convolu...

Dataset and Implementation Codes for Resilient Real-Time Metro Rerouting Using Adaptive Bidirectional Heuristics and Graph Neural Networks

This repository contains the dataset, source codes, trained graph models, and experimental outputs used in the research article entitled: "Resilient Real-Time Metro Rerouting with Adaptive Bidirectional Heuristics and Graph Neural Network-Based Edge Prediction" The provided materials support the implementation and eval...

Planting and Shaping the Neural Tree: Growing a Musical Collaborator Through Objective-Free Neuroevolution in Open-Ended Exploration

We present a live performance for experimental electronic music in which a continuously evolving neural network joins two human performers to form an improvising trio. Rather than training a generative model on pre-existing works, we use neuroevolution strategies to grow a feed-forward neural graph in real time, with n...

ActiveFusion: Fused Representations Improve Active Learning for Molecular Property Prediction

Abstract Active learning provides an efficient strategy for molecular property prediction by iteratively prioritizing compounds for experimental evaluation. However, the effectiveness of active learning pipelines depends strongly on the choice of molecular representation, and systematic understanding of how representat...

RatKpContext Benchmark (RKpC Benchmark): Aggregate Evaluation Outputs and Reproducibility Metadata for Tissue-Context-Aware Rat Kp Prediction

The RatKpContext Benchmark (RKpC Benchmark) is the data companion to the manuscript “Dissecting Tissue-Context Effects in Graph Neural Networks for Rat Kp Prediction: Additive Shifts, Fusion Position, and Extrapolation.” It contains aggregate evaluation outputs for four graph neural network architectures under repeated...