graph neural networks

Centralized System for Automatically Restoring Normal Operation of a Power District with Distributed Energy Resources

The article deals with the development of principles for organizing centralized control for automatically restoring and optimizing the normal operation of power districts with distributed energy resources (DER). The aim of the study is to achieve more reliable operation of distribution power grids through making a shif...

AI-SOLO: A hybrid framework for cognitive assessment in programming education

Traditional programming assessments focus on correctness and efficiency and do not consider students' cognitive learning processes or code structure. Teachers then encounter challenges in providing effective feedback to facilitate cognitive learning in programming education. This study proposes a hybrid AI framework, A...

Noise to One, Signal to Another: Under Strong Heterophily, a Separate Self-Path Decides Whether Edges Help a GNN

When edges are removed from a graph, do graph neural networks lose performance because they lose information, or because they lose specific structural signals? We study this with a controlled edge-removal probe. We sparsify graphs from 0% to 95% under three strategies: random removal, removal of cross-label (heterophil...

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...