Abstract

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 the existing machine learning algorithms. Recently, many studies on extending deep learning approaches for graph data have emerged. In this article, 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 GNNs into four categories, namely, recurrent GNNs, convolutional GNNs, graph autoencoders, and spatial-temporal GNNs. We further discuss the applications of GNNs across various domains and summarize the open-source codes, benchmark data sets, and model evaluation of GNNs. Finally, we propose potential research directions in this rapidly growing field.

MeSH Terms

AlgorithmsData MiningHumansMachine LearningNeural NetworksComputerSurveys and Questionnaires

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Publication Info

Year
2020
Type
article
Volume
32
Issue
1
Pages
4-24
Citations
7809
Access
Closed

Social Impact

Social media, news, blog, policy document mentions

Citation Metrics

7809
OpenAlex
481
Influential
7541
CrossRef

Cite This

Zong-Han Wu, Shirui Pan, Fengwen Chen et al. (2020). A Comprehensive Survey on Graph Neural Networks. IEEE Transactions on Neural Networks and Learning Systems , 32 (1) , 4-24. https://doi.org/10.1109/tnnls.2020.2978386

Identifiers

DOI
10.1109/tnnls.2020.2978386
PMID
32217482
arXiv
1901.00596

Data Quality

Data completeness: 88%