A Comprehensive Survey on Graph Neural Networks

Resource history | v1 (current) | created by janarez on May 12, 2021

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Title: A Comprehensive Survey on Graph Neural Networks
Type: Paper
Created: 2019-12-04
Description: 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.
Link: http://arxiv.org/abs/1901.00596
Identifier: ISSN: 2162-237X, 2162-2388

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