graph neural networks

Graph Theory Applications In Artificial Intelligence And Data Science

Graph Theory has been established as a fundamental approach to modeling relational data in Artificial Intelligence and Data Science. In this paper, a thorough study of graph-related methods has been conducted with a special focus on Graph Neural Networks (GNNs) and its variants for processing non-Euclidean data structu...

Skeleton-based sign language recognition via bone-augmented graph neural networks and temporal modeling

Sign language recognition is a challenging task that requires understanding both spatial relationships between body joints and temporal movement patterns. This paper proposes a hybrid skeleton-based architecture combining Graph Neural Networks (GNN) and 1D Convolution for American Sign Language (ASL) recognition, desig...

GNN-Based Social Media Forensics

Social media websites have not only become the centre of human communication in the digital era, but also include such complicated issues as misinformation distribution, organised manipulation, and fraud. Proper analysis and comprehension of the inner mechanics and framework of such sites is a key to digital security a...

Code for: Selecting Descriptor Models and Graph Neural Networks For Structural Response Prediction Of Varying Geometric Complexity

This paper compares descriptor models and geometric point graph neural networks for structural response prediction in procedurally generated L-shaped brackets with zero to eight holes. Complexity is quantified using hole count, removed-area fraction, boundary multiplier, inverse compactness and normalized minimum ligam...

Evaluating Depth-Based Human Pose Estimation in Real-World Nursing Home Environments

Human pose estimation in real-world indoor environments remains challenging due to privacy concerns, occlusions, varying numbers of people, and noisy sensor data. In this work, we investigate pose estimation from depth images captured over an extended period in a nursing home setting. We explore multiple input represen...

Code and data for "Polymer Property Prediction via an Automated Molecular Dynamics Pipeline and Transfer Learning"

Code and data accompanying the manuscript Polymer Property Prediction via an Automated Molecular Dynamics Pipeline and Transfer Learning (J. N. Law, D. Lazarenko, T. Bernat, B. C. Knott, M. R. Shirts). The study builds short oligomers (trimers) directly from monomer SMILES and a polymerization mechanism, parameterizes...

KG-Orchestrator Graph Neural Network-Driven Resource Orchestration for 6G Distributed Networks

The highly distributed infrastructure and the hetero geneous services and dynamic workloads of future 6G networks impose severe requirements on resource orchestration. We in troduce KG-Orchestrator, a unified framework which combines a Neo4j knowledge graph and a multi-GNN ensemble (Graph SAGE, HashGNN, GAT, GCN) to pe...

Anomaly detection of drilling overflow using Shapelet-aware Graph Temporal Network

Artificial Intelligence Reveals How Alloying Amount Turns Electronic Structure into Mechanical Performance

This repository provides the complete computational workflow for AlloyGCN-based prediction, surrogate modeling, and explainable analysis of Cantor alloys with additional alloying elements. The pipeline includes: Graph neural network (AlloyGCN) training and evaluation for process-dependent mechanical property prediction...

An Interpretable AI-Based Smart Engineering Framework for Carbon Emission Prediction in Coastal Port-Industrial Zones

Carbon emission prediction of coastal port-industrial zones integrates industrial production, maritime logistics and spatial environmental governance. Existing models fail to simultaneously capture multi-scale temporal fluctuations and spatial correlations of industrial units under meteorological and policy interferenc...

Explainable Spatio-Temporal Graph Neural Network for Urban Land Surface Temperature Prediction and Thermal Hotspot Assessment

The relationships between land cover, vegetation, urban morphology, and the atmosphere play a significant role in shaping urban land surface temperatures. Precise prediction of such spatiotemporal variations in thermal patterns is essential for assessing health risks and planning for urban climate resilience. The study...

Application of Graph Neural Networks in Digital Logic Circuit Optimization

Abstract Digital logic circuits are becoming increasingly complex, making efficient circuit optimization an important part of Electronic Design Automation (EDA). Traditional circuit optimization methods may require extensive computation and predefined rules when dealing with complex circuit structures. This study aims...