Deep Learning on Graphs: A Survey

Deep learning has been shown to be successful in a number of domains, ranging from acoustics, images, to natural language processing. However, applying deep learning to the ubiquitous graph data is non-trivial because of the unique characteristics of graphs. Recently, substantial research efforts ha...

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Bibliographic Details
Published in:IEEE transactions on knowledge and data engineering 2022-01, Vol.34 (1), p.249-270
Main Authors: Zhang, Ziwei, Cui, Peng, Zhu, Wenwu
Format: Article
Language:English
Subjects:
Acoustics
Deep learning
Distance measurement
graph autoencoder
graph convolutional network
Graph data
graph neural network
Graph neural networks
Graphs
Natural language processing
Recurrent neural networks
Reinforcement learning
Social networking (online)
Task analysis
Teaching methods
Training
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Summary:Deep learning has been shown to be successful in a number of domains, ranging from acoustics, images, to natural language processing. However, applying deep learning to the ubiquitous graph data is non-trivial because of the unique characteristics of graphs. Recently, substantial research efforts have been devoted to applying deep learning methods to graphs, resulting in beneficial advances in graph analysis techniques. In this survey, we comprehensively review the different types of deep learning methods on graphs. We divide the existing methods into five categories based on their model architectures and training strategies: graph recurrent neural networks, graph convolutional networks, graph autoencoders, graph reinforcement learning, and graph adversarial methods. We then provide a comprehensive overview of these methods in a systematic manner mainly by following their development history. We also analyze the differences and compositions of different methods. Finally, we briefly outline the applications in which they have been used and discuss potential future research directions.
ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2020.2981333