典型文献
Graph Neural Architecture Search:A Survey
文献摘要：
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.
文献关键词：
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作者姓名：
Babatounde Moctard Oloulade;Jianliang Gao;Jiamin Chen;Tengfei Lyu;Raeed Al-Sabri
作者机构：
School of Computer Science and Engineering,Central South University,Changsha 410083,China
文献出处：
引用格式：
[1]Babatounde Moctard Oloulade;Jianliang Gao;Jiamin Chen;Tengfei Lyu;Raeed Al-Sabri-.Graph Neural Architecture Search:A Survey)[J].清华大学学报自然科学版（英文版）,2022(04):692-708
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AB值：
0.582733
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