A Comprehensive Survey on Graph Neural Networks


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A Comprehensive Survey on Graph Neural Networks

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Title
A Comprehensive Survey on Graph Neural Networks
Type
Paper
Created
2019
Description
We propose a new taxonomy to divide the state-of-the-art graph neural networks into four categories, namely recurrent graph neural networks, convolutional graph neural networks, graph autoencoders, and spatial-temporal graph neural networks. We further discuss the applications of graph neural networks across various domains and summarize the open source codes, benchmark data sets, and model evaluation of graph neural networks. Finally, we propose potential research directions in this rapidly growing field.
Link
http://arxiv.org/abs/1901.00596
Identifier
ISSN: 2162-237X, 2162-2388

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