graph neural networks

A Bayesian-network-guided spatiotemporal model for ship navigation-status prediction in the Three Gorges Reservoir Area

The Three Gorges Reservoir Area is a critical bottleneck in China's inland waterway system. Rapid hydrological variation and dense vessel traffic can quickly change whether cargo ships are permitted to navigate in the regulated reach between the Three Gorges Dam and Gezhouba Dam. Reliable prediction of future navigatio...

Explainable Artificial Intelligence for Real-Time Stability Assessment in Smart Power Systems

INTRODUCTION: Smart grid stability analysis is necessary for reliable power system operations in the presence of increased renewable energy penetration and distributed energy resources. OBJECTIVES: This study proposes a novel Graph Transformer–Temporal Fusion Transformer (GT-TFT) with an Explainable Artificial Intellig...

Correction to: Recurrent Graph Neural Network Hybrid Model for Spatio-Temporal Traffic Flow Prediction in Intelligent Transportation Systems

IL-FormAI: A machine learning-guided framework for screening mRNA-LNP formulations toward improved in vivo expression

The mRNA delivery efficiency of lipid nanoparticles (LNPs) is governed by the chemical structure of ionizable lipid (IL) and formulation. Appropriate formulation optimization may help realize the delivery potential of IL, but current formulation screening methods for newly designed ILs still rely on wet-lab experiments...

NETWORK MODELS FOR IDENTIFYING MENTAL HEALTH DISORDERS

Mental health disorders, much like the neural system responsible for them, are multidimensional concepts of compounding complexity. Characterising psychopathology may require examining large-scale network organisation rather than isolated regions or small-scale networks. Despite the growth of network neuroscience as...

Hybrid Spatio-Temporal Crime Forecasting with Graph Neural Networks

Traditional crime forecasting models often struggle with the complex, interconnected dynamics of urban crime, primarily because they analyze spatial and temporal factors in isolation. This paper introduces a novel hybrid deep learning framework designed to overcome these limitations by modeling crime as a dynamic spati...

Trust-Aware Federated Learning for Privacy-Preserving IoT Intrusion Detection

FLZT-IDS (Federated Learning with Zero-Trust for Intrusion Detection in IoT) employs gradient-deviation monitoring to mitigate the effects of heterogeneous client updates, while Graph Neural Networks and an Attention-based BiLSTM capture relational and temporal attack patterns.

Explainable Disease Classification via Multi-Ultrasound Images using Graph Neural Networks

Graph Neural Networks (GNNs) have shown promise in computer-aided diagnosis from multiple ultrasound images. However, their patient-level predictions do not reveal which images contribute to the result, limiting interpretability. We present an explanation framework that adapts Local Interpretable Model-agnostic Explana...

MalGraphNet: an adversarially robust heterogeneous graph neural network for behaviour-based malware classification using API semantics and control flow topology

Ranking Sparse Candidate Sets: Phase Transitions in Learned TSP Graph Pruning

Dynamic user interest modeling and prediction integrating query box embedding with graph neural networks

User interests are multi-peaked, vaguely bounded, and prone to temporal drift, yet most recommendation models still encode them as single points in a latent space, discarding coverage and uncertainty alike. This paper represents interest as a region rather than a location, and lets that region move as the interaction r...

Graduate Destination Prediction Using an LLM-Enhanced Heterogeneous Graph Neural Network