A Comprehensive Survey on Graph Neural Networks

Resource | v1 | created by jjones |
Type Paper
Created 2019
Identifier ISSN: 2162-237X, 2162-2388


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.


about Graph neural network

A graph neural network (GNN) is a class of neural networks for processing data represented by graph d...

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