Graph Neural Networks and its Applications

Graph Neural Network and Some of GNN Applications - neptune.ai

Graph Neural Networks (GNNs) are a class of deep learning methods designed to perform inference on data described by graphs.

Applications of Graph Neural Networks (GNN) | by Jonathan Hui

A DNN can be trained on hundreds of thousands of chemical structures to encode and decode molecules, as well as building predictors that ...

Graph Neural Network Applications and its Future - XenonStack

What are the Real-World Applications of Graph Neural Network? · Drug Discovery: GNNs can be used to predict the potency of potential drugs by ...

AI trends in 2024: Graph Neural Networks - AssemblyAI

Let's discover how this cutting-edge technology is powering production applications and may be shaping the future of AI.

Graph Neural Network and its applications - IOPscience

This paper mainly provides an overview of the current research status of graph neural networks and proposes future work from three aspects.

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 ...

Applications of Graph Neural Networks (GNNs) - Frontiers

Graph Neural Networks (GNNs) are a recent family of Neural Network models specifically designed to harness the inherent structure and dependencies present in ...

Graph Neural Networks and its Applications - Seldon

Graph Neural Networks are a type of artificial neural network which are designed to process graph structured data.

Graph Neural Networks and Their Current Applications in ... - Frontiers

Graph Neural Networks and Their Current Applications in Bioinformatics ... Graph neural networks (GNNs), as a branch of deep learning in non-Euclidean space, ...

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 ...

Graph Neural Networks (GNN) — Concepts and Applications

We have seen the power of Machine Learning in drawing insights about data (Unsupervised Learning) or predicting new outcomes (Supervised ...

Graph Neural Networks and Their Current Applications in ... - NCBI

Abstract. Graph neural networks (GNNs), as a branch of deep learning in non-Euclidean space, perform particularly well in various tasks that ...

What Are Graph Neural Networks? - NVIDIA Blog

“GNNs are one of the hottest areas of deep learning research, and we see an increasing number of applications take advantage of GNNs to improve ...

Graph Neural Networks: Methods, Applications, and Opportunities

In the last decade or so, we have witnessed deep learning reinvigorating the machine learning field. It has solved many problems in the domains ...

What is Graph Neural Network? An Introduction to GNN and Its ...

DeepWalk is a new graph neural network that operates directly on the target graph structure. It uses a randomised path-traversing technique to ...

Graph Neural Network and its applications - Semantic Scholar

An overview of the current research status of graph neural networks and proposed improved algorithms to further promote breakthroughs in more applications ...

Graph Neural Network and Its Applications - IGI Global

Graphs have more expressive power than any other data structure. Graph neural network is one of the application areas of deep learning, and it has applications ...

Graph neural network - Wikipedia

Relevant application domains for GNNs include natural language processing, social networks, citation networks, molecular biology, chemistry, physics and NP-hard ...

(PDF) Graph Neural Network and its applications - ResearchGate

This is a new research hotspot— Graph Neural Networks (GNNs). It is necessary to summarize the latest GNNs-related studies and propose improved algorithms to ...

Graph Neural Networks: Models and Applications

Graph Neural Networks (GNNs), which generalize the deep neural network models to graph structured data, pave a new way to effectively learn representations for ...