Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 3
668-674

Ensemble learning framework for graph neural network with feature and structure enhancement

Zhang Jiajie1
Guo Yi1,2,3
Wang Jiahui1
Wang Yu1
1. Dept. of Computer Science & Engineering, East China University of Science & Technology, Shanghai 200237, China
2. Business Intelligence & Visualization Research Center, National Engineering Laboratory for Big Data Distribution & Exchange Technologies, Shanghai 200436, China
3. Shanghai Engineering Research Center of Big Data & Internet Audience, Shanghai 200072, China
DOI: 10.19734/j.issn.1001-3695.2021.09.0352

Abstract

Recently, graph neural networks receive widespread attention due to their rich representation and reasoning capabilities. To best knowledge, current research mainly focuses on amending the convolutional strategy and network structure for higher performance, so the performance will be inevitably constrained by the limitations of the single model. Inspired by the idea of ensemble learning, this paper innovatively proposed an ensemble learning framework for graph neural network(EL-GNN). Unlike regular text and images, graph data not only had features but also had rich topology information. Therefore, EL-GNN additionally supplemented the structure information during the ensemble stage rather than merely integrating the prediction results of independent classifiers. Besides, this paper further revised the ensemble strategy through reconstructing a feature-level similarity graph for subsequent assembling, which balanced the feature and structure information on the basis of the assumptions of those nodes with the similar feature or easy reachability of high probability to share the same labels. The comprehensive experiments indicate that the proposed ensemble strategy achieves an impressive performance and EL-GNN is superior to other off-the-shelf models on the node classification task.

Foundation Support

国家重点研发计划资助项目(2018YFC0807105)
国家自然科学基金资助项目(61462073)
上海市科学技术委员会科研计划项目(17DZ1101003,18511106602,18DZ2252300)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.09.0352
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 3
Section: Algorithm Research & Explore
Pages: 668-674
Serial Number: 1001-3695(2022)03-004-0668-07

Publish History

[2021-11-29] Accepted Paper
[2022-03-05] Printed Article

Cite This Article

张嘉杰, 过弋, 王家辉, 等. 基于特征和结构信息增强的图神经网络集成学习框架 [J]. 计算机应用研究, 2022, 39 (3): 668-674. (Zhang Jiajie, Guo Yi, Wang Jiahui, et al. Ensemble learning framework for graph neural network with feature and structure enhancement [J]. Application Research of Computers, 2022, 39 (3): 668-674. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    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.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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