graph neural networks

zhangyinning/AtomSight-GNN: AtomSight-GNN

This repository accompanies the paper: Yinning Z., et al., "AtomSight-GNN: a global feature-attention layer that extracts reproducible, property-specific chemical drivers from crystal graph neural networks" (2026). It contains the source code and data pipeline for AtomSight-GNN, which integrates a global feature-dimens...

neuroVIISAS software for windows and unix

This Figshare record provides the current executable releases of neuroVIISAS for Microsoft Windows and Linux.neuroVIISAS is a Java-based software environment for the construction, visualization, and quantitative analysis of neuroanatomical connectivity networks. The software supports the integration of anatomically def...

QGSL-GNSS: Q-Learning-Based Adaptive Graph Structure Learning for Semi-Supervised GNSS Interference Classification

Jamming devices often pose a significant threat to Global Navigation Satellite System (GNSS) signals by disrupting position navigation and timing (PNT) services. Consequently, reliable detection and classification of interference types are essential for developing effective anti-jamming strategies. However, most existi...

MicroWeaver: A Constraint-Guided Structure–Semantic Aware Approach to Automated Microservice Decomposition

Microservice architecture is a pivotal paradigm for refactoring monolithic systems to improve maintainability and evolvability. However, automated, explainable decomposition remains a difficult task, especially when balancing structural soundness with semantic consistency in complex legacy systems. We present MicroWeav...

Learning to Triage Vulnerability Reports from Program Analysis: An Empirical Study in Node.js

Program analysis tools often produce large volumes of candidate vulnerability reports that require costly manual review, creating a practical challenge: how can security analysts prioritize the reports most likely to be true vulnerabilities? This paper investigates whether machine learning can be applied to prioritizin...

Nowcasting officially reported highly pathogenic avian influenza outbreak counts under heterogeneous reporting delays

Official animal disease series are often incomplete due to delays in reporting. This retrospective observational study evaluated whether increased model complexity enhances probabilistic nowcasts of eventual officially reported outbreak-equivalent counts. We reconstructed historical information states from a fixed extr...

Replication package: evaluation protocol validity in graph neural network link prediction on sparse collaboration networks

Derived datasets, per-run experiment outputs, and analysis code for a study benchmarking graph neural network link prediction under alternative evaluation protocols on sparsely sampled collaboration networks, including a bibliographic co-authorship validation, a synthetic preferential-attachment mechanism simulation, h...

DrugMiR: A Multi-Source Heterogeneous Graph Neural Network with Adaptive Feature-Embedding Fusion for Predicting miRNA-Mediated Drug Resistance and Sensitivity

Bond-Level Prediction of U-Donor Bond Lengths in Uranyl Coordination Complexes Using Machine Learning for Ligand Recognition

Abstract U-donor bond lengths in uranyl coordination complexes directly reflect metal–ligand interaction strength and provide important structural parameters for evaluating ligand recognition, selective complexation, and extraction/adsorption system design. To enable bond-level prediction, this study developed a DFT-de...

Core code and synthetic examples for characterizing three-dimensional urban canopy structure in Shanghai

This record provides author-developed core code and synthetic examples accompanying the manuscript “Characterizing three-dimensional urban canopy structure in Shanghai with spatial graph learning”. The package includes implementations for attribute-space and spatially regularized K-means, graph neural network encoders,...

Harnessing artificial intelligence for fleet decarbonization: a Tunisian case study using deep reinforcement learning and graph neural networks

NORMAN aquatic ecotoxicity model v0.1: QMRF, QPRF example, model card and SusDat predictions

A multi-species model for the acute and chronic aquatic toxicity of organic chemicals, developed for the NORMAN network. It predicts toxicity values (log10 µg/L) for 121 species-and-endpoint combinations (heads), of which 82 are released for use (59 acute: LC50/EC50; 23 chronic: NOEC/EC10), covering fish, crustaceans,...