A review of graph neural networks: concepts, architectures, techniques, challenges, datasets, applications, and future directions

A review of graph neural networks: concepts, architectures, techniques, challenges, datasets, applications, and future directions

Abstract Deep learning has seen significant growth recently and is now applied to a wide range of conventional use cases, including graphs. Graph data provides relational information between elements and is a standard data format for various machine learning and deep learning tasks. Models that can...

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Bibliographic Details
Main Authors: Bharti Khemani, Shruti Patil, Ketan Kotecha, Sudeep Tanwar
Format: Article
Language:English
Published: SpringerOpen 2024-01-01
Series:Journal of Big Data
Subjects:
Graph Neural Network (GNN)
Graph Convolution Network (GCN)
GraphSAGE
Graph Attention Networks (GAT)
Message Passing Mechanism
Natural Language Processing (NLP)
Online Access:https://doi.org/10.1186/s40537-023-00876-4

Internet

https://doi.org/10.1186/s40537-023-00876-4

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