graph neural networks

Oscillatory Neural Dynamics over Sheaves

Effective long-range propagation remains a central challenge in graph neural networks, as increasing a model's propagation depth does not guarantee that distant nodes effectively influence each other. Sheaf neural networks enrich graph propagation through matrix-valued transport between stalks; still, this expressivity...

Node-level Graph Neural Architecture Search Framework

In recent years, Graph Neural Networks (GNNs) and architecture search frameworks have gained extensive application in non-Euclidean data processing, attributable to their superior capacity in managing unstructured data. Nevertheless, traditional approaches typically apply uniform convolution operations to all nodes, re...

Sparse weighted graph convolutional networks for colorectal cancer classification using Hi-C data

Colorectal cancer remains a leading cause of global mortality, driving demand for precise screening methodologies that leverage complex genomic architectures. The functional interplay between spatial chromatin organization and regulatory networks is increasingly recognized as an important component of malignant trans...

A hybrid chemoinformatics approach integrating caputo fractional-derivative graph invariants and artificial neural network for modeling of anti-sickle cell compounds

Cheminformatics focuses on the accurate prediction of physicochemical properties, which plays a crucial role in molecular modeling and materials science, as experimental determination is often time-consuming and costly. In this study, we propose a machine learning based framework for predicting physicochemical prop...

Recognition Method for Road Grid Pattern By Integrating GraphSAGE Model and Gating Mechanism

Grid pattern recognition is of great significance for spatial pattern cognition, cartographic generalization, and multi-scale representation. To address the issues that existing methods rarely consider multi-level features and insufficiently utilize the learning and mining capabilities of intelligent models, this paper...

Recognition Method for Road Grid Pattern By Integrating GraphSAGE Model and Gating Mechanism

Grid pattern recognition is of great significance for spatial pattern cognition, cartographic generalization, and multi-scale representation. To address the issues that existing methods rarely consider multi-level features and insufficiently utilize the learning and mining capabilities of intelligent models, this paper...

Data and Trained Model Weights of the "Accelerating dynamic polarizability calculations of organic molecules using equivariant graph neural networks" paper

Data and trained model weights for "Accelerating dynamic polarizability calculations of organic molecules using equivariant graph neural networks". This record contains the datasets and the trained model weights used in the manuscript. The code is available on GitHub: https://github.com/aimat-lab/DynPolDetanet Contents...

A deep learning model integrating multi-phenotypic features of microcalcifications enhances malignancy prediction for BI-RADS 4 microcalcifications in mammography: a multicenter study

Some Breast Imaging Reporting and Data System (BI-RADS) 4 category microcalcifications (MCs) exhibit atypical or overlapping characteristics in terms of morphology and distribution, posing a diagnostic challenge for doctors. The aim was to develop and evaluate a deep learning (DL) model for predicting the malignancy of...

OpenFOAM data of the lid-driven cavity, natural-convection cavity, heated cylinder wake and data center benchmarks

OpenFOAM data of the benchmarks of the manuscript "Label-Free Finite-Volume-Residual Training of Attention Graph Neural Networks for Coupled Thermo-Fluid Fields".

Validating and Correcting Graph Neural Network Attention for Drug-Induced Liver Injury Prediction

Attention-based graph neural networks (GNNs) are increasingly used for drug-induced liver injury (DILI) and other toxicity prediction tasks on the claimed strength of built-in interpretability, but this claim is almost always supported by a handful of hand-selected examples rather than being tested systematically. Us...

ReactionGraphNeuralNetwork: graph neural network surrogate models for reaction properties

Code for graph neural network-based surrogate models used to predict reaction-related properties for reinforcement-learning-accelerated atomistic simulations.

Using Graph Neural Networks (GNN) and Gated Recurrent Unit (GRU) based on Regression Modelling for Climatic Forecasting