Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 7
2013-2018

Multi-teacher learning graph neural network based on feature and graph structure information augmentation

Zhang Jiajie1
Guo Yi1,2,3
Wang Jiahui4
1. School of Information Science & Engineering, East China University of Science & Technology, Shanghai 200237, China
2. National Engineering Laboratory for Big Data Distribution & Exchange Technologies, Business Intelligence & Visualization Research Center, Shanghai 200436, China
3. Shanghai Engineering Research Center of Big Data & Internet Audience, Shanghai 200072, China
4. School of Computer & Information Engineering, Shanghai Polytechnic University, Shanghai 201209, China
DOI: 10.19734/j.issn.1001-3695.2022.11.0765

Abstract

In recent years, the powerful representation and modeling capabilities of graph neural networks for graph data have made them widely used in many fields and made breakthroughs. However, the existing models tend to optimize the graph convolution aggregation strategy and network structure and lack the exploration of the prior knowledge of the graph data itself. In response to the above problems, this paper designed a multi-teacher learning graph neural network based on feature information and structural information enhancement through the method of knowledge distillation, which broke the limitations of existing models for data prior knowledge extraction. Given the rich features and structural information behind the graph data, this paper designed the data enhancement methods of node feature and edge respectively. On this basis, knowledge embedding was performed on the original data and the enhanced data through the multi-teacher learning module, so that the student model could learn more prior knowledge about the data. On the Cora, Citeseer, and PubMed datasets, the node classification accuracy increased by 1%, 1.3% and 1.1%, respectively. Experimental results demonstrate that the information-augmented multi-teacher learning model proposed in this paper can effectively capture prior knowledge.

Foundation Support

上海市科学技术委员会科研计划项目(22DZ1204903,22511104800)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.11.0765
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 7
Section: Algorithm Research & Explore
Pages: 2013-2018
Serial Number: 1001-3695(2023)07-014-2013-06

Publish History

[2023-02-15] Accepted Paper
[2023-07-05] Printed Article

Cite This Article

张嘉杰, 过弋, 王家辉. 基于特征和图结构信息增强的多教师学习图神经网络 [J]. 计算机应用研究, 2023, 40 (7): 2013-2018. (Zhang Jiajie, Guo Yi, Wang Jiahui. Multi-teacher learning graph neural network based on feature and graph structure information augmentation [J]. Application Research of Computers, 2023, 40 (7): 2013-2018. )

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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