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Open Access

Graph Neural Architecture Search: A Survey

School of Computer Science and Engineering, Central South University, Changsha 410083, China
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Abstract

In academia and industries, graph neural networks (GNNs) have emerged as a powerful approach to graph data processing ranging from node classification and link prediction tasks to graph clustering tasks. GNN models are usually handcrafted. However, building handcrafted GNN models is difficult and requires expert experience because GNN model components are complex and sensitive to variations. The complexity of GNN model components has brought significant challenges to the existing efficiencies of GNNs. Hence, many studies have focused on building automated machine learning frameworks to search for the best GNN models for targeted tasks. In this work, we provide a comprehensive review of automatic GNN model building frameworks to summarize the status of the field to facilitate future progress. We categorize the components of automatic GNN model building frameworks into three dimensions according to the challenges of building them. After reviewing the representative works for each dimension, we discuss promising future research directions in this rapidly growing field.

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Tsinghua Science and Technology
Pages 692-708
Cite this article:
Oloulade BM, Gao J, Chen J, et al. Graph Neural Architecture Search: A Survey. Tsinghua Science and Technology, 2022, 27(4): 692-708. https://doi.org/10.26599/TST.2021.9010057
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Received: 15 May 2021
Revised: 15 July 2021
Accepted: 30 July 2021
Published: 09 December 2021
© The author(s) 2022

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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