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Deep Learning on Graphs: A Survey

Zhang, Ziwei ; Cui, Peng ; Zhu, Wenwu

IEEE transactions on knowledge and data engineering, 2022-01, Vol.34 (1), p.249-270 [Periódico revisado por pares]

IEEE

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Título:
Deep Learning on Graphs: A Survey
Autor: Zhang, Ziwei ; Cui, Peng ; Zhu, Wenwu
Assuntos: Deep learning ; Distance measurement ; graph autoencoder ; graph convolutional network ; Graph data ; graph neural network ; Recurrent neural networks ; Reinforcement learning ; Social networking (online) ; Task analysis ; Training
É parte de: IEEE transactions on knowledge and data engineering, 2022-01, Vol.34 (1), p.249-270
Descrição: 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.
Editor: IEEE
Idioma: Inglês

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Deep Learning on Graphs: A Survey

Zhang, Ziwei ; Cui, Peng ; Zhu, Wenwu

IEEE

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