graph neural networks

Interpretable Multimodal Engagement Prediction with Graph-based Mixture-of-Experts

A graph-based Mixture-of-Experts (MoE) framework that explicitly separates self and social influences to enable interpretable engagement modeling, which outperforms baselines by up to 64.1% while offering interpretable insights into engagement dynamics across language and gender groups.

Attention-enhanced graph neural networks for nonlinearity compensation in optical fiber communication

This paper presents AEGNN (attention-enhanced graph neural network), framework that integrated Graph Attention Networks (GAT) with multi-head self-attention mechanisms to model space-time topology of optical networks and is the first fully reproducible framework combining attention-based GNNs with open-source optical c...

Causal Graph Constrained LLM for Fault Diagnosis in Industrial Data Centers

Industrial data-center systems contain complex component dependencies and long fault-propagation chains, making accurate root-cause localization difficult. Large language models (LLMs) provide a promising solution because of their strong ability to understand, organize, and reason over heterogeneous operational evidenc...

Graph Neural Network-Based Analysis for 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...

Intelligent Digital Logic Circuit Optimization Through Graph Neural Networks

Abstract The increasing complexity of digital logic circuits creates a need for effective methods of circuit analysis and optimization. Conventional Electronic Design Automation (EDA) techniques commonly depend on established algorithms and predefined rules, which can become difficult to apply to more complicated circu...

UEXGNN: reference inference release of a physically constrained autoregressive graph neural network for transient-field prediction of near-seabed underwater explosions

Strain-Engineered Magnetism, Super-Exchange Physics, and Thermal Phase Transitions in Monolayer Chromium Trihalides CrX3 (X = Cl, Br, I)

This repository contains the Python calculation scripts, raw data JSON files, and publication-quality vector figures supporting the manuscript: "Strain-Engineered Magnetism, Super-Exchange Physics, and Thermal Phase Transitions in Monolayer Chromium Trihalides CrX3 (X = Cl, Br, I)" ### Repository Structure & Contents:...

Smart Circuit Structure Learning for Digital Logic Optimization

Abstract The increasing scale and interconnectedness of digital logic designs make it more difficult to examine their internal organization and determine where improvements can be made. Conventional Electronic Design Automation (EDA) methods generally depend on established algorithms and rule-driven procedures, which m...

Mapping Urban Mixed Land Use via Multimodal Fusion and Large Language Models

The precise delineation of urban land use, particularly mixed land use, is essential for sustainable urban planning and resource allocation. However, traditional pixel-based mapping methods face a profound "semantic gap" and struggle to decode the complex, overlapping functional dynamics of modern cities. This study pr...

Interpretable attention-deficit hyperactivity disorder diagnosis from functional magnetic resonance imaging via hybrid convolutional neural network–transformer learning and multi-level graph-based comprehensibility

Attention-Deficit Hyperactivity Disorder (ADHD) is a disorder about the brain development with various changes in the brain structure, involving constant diagnosis using predictable neuroimaging analysis. Current deep learning methods appear to show promising performance in computerized ADHD classification using magnet...

Predicting vehicle operators’ neck discomfort under vibration using head posture data: A dynamic two-stage attention-based method

Vehicle vibration degrades interaction with In-Vehicle Information Systems (IVIS) by increasing neck discomfort and head-posture instability. We ran three laboratory experiments manipulating interface context while applying 0–2.5 Hz (Hz) vertical vibration. Participants rated neck discomfort relative to vibration on th...

Smart Circuit Structure Learning for Digital Logic Optimization

Abstract The increasing scale and interconnectedness of digital logic designs make it more difficult to examine their internal organization and determine where improvements can be made. Conventional Electronic Design Automation (EDA) methods generally depend on established algorithms and rule-driven procedures, which m...