graph neural networks

Deep Learning-Based Intelligent Early Warning System for Corporate Financial Risk

With the advancement of computer technology, corporate financial risk control systems face challenges related to complex data processing and delays in risk identification. This paper proposes an intelligent risk control method based on a hybrid LSTM-GNN (Long Short-Term Memory-Graph Neural Network) model, utilizing LST...

A Hybrid Learning Framework for Automated SAT Solver Selection

Boolean Satisfiability (SAT) solving underlies a wide range of practical applications, including hardware verification, software testing, automated planning, and combinatorial design. Decades of engineering effort have produced highly optimised Conflict-Driven Clause Learning (CDCL) solvers, yet empirical studies consi...

1113wu/PE-Based-Defense: PE-Based deformable

Source code for paper: Degraded but Not Entirely Ineffective: PE-Based Deformable Graph Neural Networks

Prediction of vacancy formation energies in Ni-based superalloys by density functional theory calculations and machine learning

Thermal vacancies play a critical role in high-temperature Ni-based superalloys, influencing elastic constants, creep resistance, oxidation resistance, etc. Local chemical variations in multicomponent alloys generate a broad distribution of vacancy formation energies, producing low-energy states that increase vacancy c...

DM-GAE: A Dual-Masked Graph Autoencoder for Mineral Prospectivity Mapping Under Label-Limited Conditions

Physics-Informed Neural Networks and Graph Neural Networks for the Numerical Modeling of the Vector Helmholtz Equation

This workinvestigates neural network representations of vector fields governed by the vector Helmholtz equation, with particular attention on domains containing discontinuous material properties. The de Rham complex and its discrete Whitney–Nédélec counterpart are considered to clarify the relationship between the regu...

Spatio-Temporal Graph Neural Networks for UAV Swarm Intrusion Detection

Fault tolerant cluster-based routing in wireless sensor network using novel intelligent deep learning methodology

Wireless Sensor Networks (WSNs) are highly susceptible to cluster head (CH) failures due to limited node energy and unstable communication links, resulting in data loss and reduced network reliability. This work presents a fault-tolerant cluster-based routing model that integrates an Improved Graph Neural Network (IGNN...

Steady-State Visual-Evoked EEG Dataset for Visual Function Assessment via Biomarker Extraction

Amblyopia and intermittent exotropia (IXT) are prevalent pediatric visual impairments characterized by deficits in cortical visual processing. Understanding the underlying neurophysiological mechanisms and tracking neural recovery requires high–quality, objective data, yet open–access EEG datasets specifically designed...

HUNTINGTON'S DISEASE ENRICHED COHORT

This Zenodo record contains a free, evaluation-grade sample of 10,000 synthetic patient records — a genuine, unmodified subset of the full production dataset, not a separate re-generation. It is provided so that researchers, ML engineers, and clinical data scientists can inspect the schema, phenotypic depth, and biomar...

Information Cascade Popularity Prediction Using Structural and Temporal Features

The basic ideas of online diffusion are introduced by formalising cascade and global graphs, and the extraction of structural and temporal features is described, and the shift toward modelling stochastic cascade dynamics is highlighted.

GeoNicheTrans: Spatial Niche Inference and Immune Architecture Characterization in Oral and Oropharyngeal Squamous Cell Carcinoma Validated by CODEX Protein Profiling

Background: The tumor microenvironment is spatially organized into distinct ecological niches that shape immune responses and therapeutic outcomes, yet inferring spatial niches from transcriptomic data and validating them at the protein level remains challenging, particularly in oral and oropharyngeal squamous cell car...