Graph Neural Network Operators: a Review

Graph Neural Networks (GNN) is one of the promising machine learning areas in solving real world problems such as social networks, recommender systems, computer vision and pattern recognition. One of the important component of GNN is GNN operators which are responsible to train GNN graph structured...

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Vydáno v:Multimedia tools and applications Ročník 83; číslo 8; s. 23413 - 23436
Hlavní autoři: Sharma, Anuj, Singh, Sukhdeep, Ratna, S.
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.03.2024
Springer Nature B.V
Témata:
Algorithms
Classification
Computer Communication Networks
Computer Science
Computer vision
Data Structures and Information Theory
Datasets
Graph neural networks
Graphs
Machine learning
Multimedia
Multimedia Information Systems
Neural networks
Operators (mathematics)
Pattern recognition
R&D
Recommender systems
Research & development
Social networks
Special Purpose and Application-Based Systems
Structured data
GNN convolutional operators
Deep learning
Graph structural representation
Graph neural networks
ISSN:1573-7721, 1380-7501, 1573-7721
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Popis
Shrnutí:Graph Neural Networks (GNN) is one of the promising machine learning areas in solving real world problems such as social networks, recommender systems, computer vision and pattern recognition. One of the important component of GNN is GNN operators which are responsible to train GNN graph structured data and forward learning nodes information to other layers. This review focus on recent advancements of GNN operators in detail. The rich Mathematical nature of GNN operators has been discussed for selected GNN operators. The review also highlights different benchmark graph structured datasets and presents results using different GNN operators. We have included thorough discussion for state-of-the-art in this field including limitations and future directions. Overall, the review covers important areas of GNN as GNN operators from future research directions point of view and real world applications perspective.
Bibliografie:ObjectType-Article-1
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ISSN:1573-7721
1380-7501
1573-7721
DOI:10.1007/s11042-023-16440-4