Au sujet de ce document
- sl:arxiv_author :
- sl:arxiv_firstAuthor : Zonghan Wu
- sl:arxiv_num : 1901.00596
- sl:arxiv_published : 2019-01-03T03:20:55Z
- sl:arxiv_summary : 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
existing machine learning algorithms. Recently, many studies on extending deep
learning approaches for graph data have emerged. In this survey, 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 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.@en
- sl:arxiv_title : A Comprehensive Survey on Graph Neural Networks@en
- sl:arxiv_updated : 2019-12-04T01:43:00Z
- sl:bookmarkOf : https://arxiv.org/abs/1901.00596
- sl:creationDate : 2019-07-15
- sl:creationTime : 2019-07-15T23:15:09Z
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