- A review of graph neural network applications in mechanics|related ...🔍
- Graph neural networks🔍
- SIAM Activity Group on Dynamical Systems on X🔍
- A review of graph neural networks🔍
- A Review of Graph Neural Networks for Recommender Applications🔍
- [2101.10025] A Review of Graph Neural Networks and Their ...🔍
- Graph Neural Networks🔍
- A Review on Graph Neural Network Methods in Financial Applications🔍
A review of graph neural network applications in mechanics|related ...
A review of graph neural network applications in mechanics-related ...
Graph neural networks (GNNs) have emerged as a promising tool to tackle these challenges by adeptly learning from graph data with irregular ...
A review of graph neural network applications in mechanics-related ...
GNNs have been used to tackle a wide range of machine-learning tasks on graph-structured data due to their unique ability to capture the ...
(PDF) A review of graph neural network applications in mechanics ...
Graph neural networks (GNNs) have emerged as a promising tool to tackle these challenges by adeptly learning from graph data with irregular ...
A review of graph neural network applications in mechanics-related ...
Mechanics-related tasks often present unique challenges in achieving accurate geometric and physical representations, particularly for non-uniform ...
Graph neural networks: A review of methods and applications
Graph neural networks (GNNs) are deep learning based methods that operate on graph domain. Due to its convincing performance, GNN has become a widely applied ...
SIAM Activity Group on Dynamical Systems on X: ""A review of graph ...
"A review of graph neural network applications in mechanics-related domains" (by Yingxue Zhao, Haoran Li, Haosu Zhou, Hamid Reza Attar, ...
A review of graph neural networks: concepts, architectures ...
Different applications may require various graph neural network (GNN) models. GNNs facilitate the exchange of information between nodes in a ...
A review of graph neural network applications in mechanics-related ...
Graph neural networks (GNNs) have emerged as a promising tool to tackle these challenges by adeptly learning from graph data with irregular underlying ...
A Review of Graph Neural Networks for Recommender Applications
Abstract: Graph neural network (GNN) is a distributional model that represents graph dependencies through message exchange between graph nodes.
A review of graph neural network applications in mechanics-related ...
Graph neural networks (GNNs) have emerged as a promising tool to address these challenges. GNNs are a type of machine learning algorithm that ...
[2101.10025] A Review of Graph Neural Networks and Their ... - arXiv
Specifically, several classical paradigms of GNNs structures (e.g., graph convolutional networks) are summarized, and key applications in power ...
Graph Neural Networks: A Review of Methods and Applications - CDN
In this survey, we provide a detailed review over existing graph neural network models, systematically categorize the applications, and propose four open ...
A Review on Graph Neural Network Methods in Financial Applications
Among the graph modeling technologies, graph neural network (GNN) models are able to handle the complex graph structure and achieve great ...
A Review of Graph Neural Networks and Their Applications in ...
Specifically, several classical para- digms of GNNs structures (e.g., graph convolutional networks) are summarized, and key applications in power systems, such.
Graph neural networks for materials science and chemistry - Nature
A recent addition to the toolbox of machine learning models for chemistry and materials science are graph neural networks (GNNs), which operate ...
A review of graph neural networks and pretrained language models ...
Hybrid GNNs and logic rules can fully utilize the advantages of graph structure and logical reasoning to improve the accuracy and performance of KGR [15].
[D] Overview of advancements in Graph Neural Networks - Reddit
A new GNN-based model for estimating the time of arrival within Google Maps (with accuracy improvements up to 50%) is just one of many ...
Must-read papers on GNN - GitHub
Graph Neural Networks: A Review of Methods and Applications. AI Open 2020. paper. Jie Zhou, Ganqu Cui, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Maosong Sun ...
Graph Neural Networks and Their Current Applications in ... - NCBI
With the rapid accumulation of biological network data, GNNs have also become an important tool in bioinformatics. In this research, a ...
[PDF] A Review of Graph Neural Networks and Their Applications in ...
A comprehensive overview of graph neural networks (GNNs) in power systems is proposed, and several classical paradigms of GNNs structures are summarized, ...