A Comprehensive Survey on Graph Neural Networks

Deep learning has revolutionized many machine learning tasks in recent years, ranging from image classification and video processing to speech recognition and natural language understanding. The data in these tasks are typically represented in the Euclidean space. However, there is an increasing num...

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Veröffentlicht in:IEEE transaction on neural networks and learning systems Jg. 32; H. 1; S. 4 - 24
Hauptverfasser: Wu, Zonghan, Pan, Shirui, Chen, Fengwen, Long, Guodong, Zhang, Chengqi, Yu, Philip S.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.01.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Cognitive tasks
Complexity
Data Mining
Deep learning
Domains
Euclidean geometry
Euclidean space
Feature extraction
graph autoencoder (GAE)
graph convolutional networks (GCNs)
Graph neural networks
graph neural networks (GNNs)
graph representation learning
Graphical representations
Humans
Image classification
Image processing
Kernel
Learning algorithms
Learning systems
Machine Learning
Natural language processing
network embedding
Neural networks
Neural Networks, Computer
Object recognition
Speech recognition
Surveys and Questionnaires
Task analysis
Taxonomy
Video data
ISSN:2162-237X, 2162-2388, 2162-2388
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Zusammenfassung:Deep learning has revolutionized many machine learning tasks in recent years, ranging from image classification and video processing to speech recognition and natural language understanding. The data in these tasks are typically represented in the Euclidean space. However, there is an increasing number of applications, where data are generated from non-Euclidean domains and are represented as graphs with complex relationships and interdependency between objects. The complexity of graph data has imposed significant challenges on the existing machine learning algorithms. Recently, many studies on extending deep learning approaches for graph data have emerged. In this article, we provide a comprehensive overview of graph neural networks (GNNs) in data mining and machine learning fields. We propose a new taxonomy to divide the state-of-the-art GNNs into four categories, namely, recurrent GNNs, convolutional GNNs, graph autoencoders, and spatial-temporal GNNs. We further discuss the applications of GNNs across various domains and summarize the open-source codes, benchmark data sets, and model evaluation of GNNs. Finally, we propose potential research directions in this rapidly growing field.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2020.2978386