Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 1
128-133

Layer-wise graph convolutional network based on self-supervised learning strategy

Sun Feng1
Yang Guanci2a
Ajith Kumar V3
Zhang Ansi2b
1. Experimental Teaching Center for Liberal Arts, Zhejiang Normal University, Jinhua Zhejiang 321004, China
2. a. Key Laboratory of Advanced Manufacturing Technology of Ministry of Education, b. School of Mechanical Engineering, Guizhou University, Guiyang 550025, China
3. The School of AI, Bangalore 560002, India
DOI: 10.19734/j.issn.1001-3695.2021.06.0250

Abstract

To solve the problem that the current graph convolutional network needs to rely on large datasets, which leads to increased time and space complexity, this research proposed a layer-wise graph convolutional network based on self-supervised learning strategy(RRLFS-L-GCN). Firstly, it added an multi-task mechanism into the layer-wise graph convolutional network(layer-wise graph convolutional network, L-GCN) to improve the generalization ability of the algorithm. Then, it designed a self-supervised learning strategy that randomly removed fixed-step links(randomly remove links with a fixed step, RRLFS). Therefore, it proposed a layer-wise graph convolutional network algorithm based on a self-supervised learning strategy. Finally, it used link prediction which was to verify the performance of RRLFS-L-GCN. Experimental results show that this algorithm has the highest recognition rate, up to 97.13%. For the Cora testset, this algorithm obtains 6.73% accuracy higher than that of the unimproved layer-wise graph convolutional network algorithm. For the PubMed testset, this algorithm obtains 8.13% accuracy higher than that of the unimproved layer-wise graph convolutional network algorithm. Compared with the graph convolutional network, it improves the recognition accuracy rate on the Citeseer dataset, which is 18.43%.

Foundation Support

国家自然科学基金资助项目(61863005,62163007)
贵州省科技计划资助项目(黔科合支撑[2019]2814,黔科合平台人才[2020]6007,[2020]4Y056,[2021]439)
贵州省高等学校集成攻关大平台建设资助项目(黔教合KY字[2020]005)
浙江师范大学实验技术开发资助项目(SJ202123)
浙江师范大学数学化改革资助项目([2021]05)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.06.0250
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 1
Section: Algorithm Research & Explore
Pages: 128-133
Serial Number: 1001-3695(2022)01-023-0128-06

Publish History

[2021-10-09] Accepted Paper
[2022-01-05] Printed Article

Cite This Article

孙峰, 杨观赐, Ajith Kumar V, 等. 基于自我监督学习策略的层智能图卷积网络 [J]. 计算机应用研究, 2022, 39 (1): 128-133. (Sun Feng, Yang Guanci, Ajith Kumar V, et al. Layer-wise graph convolutional network based on self-supervised learning strategy [J]. Application Research of Computers, 2022, 39 (1): 128-133. )

About the Journal

  • Application Research of Computers Monthly Journal
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    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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