In accordance with regulations and requirements, the editorial department's website domain has been changed to arocmag.cn. The original domain (arocmag.com) will be discontinued after Dec. 31st, 2024.
See More
Algorithm Research & Explore
|
App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 4
1130-1136

Preserving low-order and high-order relationships deep learning ensemble algorithm for graph representation

Ouyang Mengcena,b
Zhang Yinglongb
Xia Xuewenb
Xu Xingb
a. School of Computing, b. School of Physics & Information Engineering, Minnan Normal University, Zhangzhou Fujian 363000, China
DOI: 10.19734/j.issn.1001-3695.2022.08.0411

Abstract

High-quality learning low-dimensional representation of nodes in the graph is a current research hotspot. The existing shallow model methods cannot capture the nonlinear relationship of the graph structure, and the graph convolution model in the graph neural network technology will cause an over-smoothing problem. At the same time, how to determine the role of different hop number relationships in graph representation learning is also a problem that needs to be solved in the research. To solve the above problems, this paper proposed a deep learning model based on T(T>1) feedforward neural networks. The framework used deep learning models to extract the nonlinear relationship of the graph structure, and T sub-models effectively capture the local and global(higher-order) relationship information of the graph, and they gave different roles in the final vector representation to take advantage of different hop relations. Experimental results on vertex classification and link prediction tasks show that the framework is competitive with existing methods, the benchmark algorithm can be improved by about 20%.

Foundation Support

国家自然科学基金资助项目(61762036)
福建省自然科学基金项目(2021J011007,2021J011008,2022J01916)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.08.0411
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 4
Section: Algorithm Research & Explore
Pages: 1130-1136
Serial Number: 1001-3695(2023)04-027-1130-07

Publish History

[2022-11-22] Accepted Paper
[2023-04-05] Printed Article

Cite This Article

欧阳勐涔, 张应龙, 夏学文, 等. 保留低阶和高阶关系的图表示深度学习集成算法 [J]. 计算机应用研究, 2023, 40 (4): 1130-1136. (Ouyang Mengcen, Zhang Yinglong, Xia Xuewen, et al. Preserving low-order and high-order relationships deep learning ensemble algorithm for graph representation [J]. Application Research of Computers, 2023, 40 (4): 1130-1136. )

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.

Indexed & Evaluation

  • The Second National Periodical Award 100 Key Journals
  • Double Effect Journal of China Journal Formation
  • the Core Journal of China (Peking University 2023 Edition)
  • the Core Journal for Science
  • Chinese Science Citation Database (CSCD) Source Journals
  • RCCSE Chinese Core Academic Journals
  • Journal of China Computer Federation
  • 2020-2022 The World Journal Clout Index (WJCI) Report of Scientific and Technological Periodicals
  • Full-text Source Journal of China Science and Technology Periodicals Database
  • Source Journal of China Academic Journals Comprehensive Evaluation Database
  • Source Journals of China Academic Journals (CD-ROM Version), China Journal Network
  • 2017-2019 China Outstanding Academic Journals with International Influence (Natural Science and Engineering Technology)
  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
  • Source Journal of China Engineering Technology Electronic Information Network and Electronic Technology Literature Database
  • Source Journal of British Science Digest (INSPEC)
  • Japan Science and Technology Agency (JST) Source Journal
  • Russian Journal of Abstracts (AJ, VINITI) Source Journals
  • Full-text Journal of EBSCO, USA
  • Cambridge Scientific Abstracts (Natural Sciences) (CSA(NS)) core journals
  • Poland Copernicus Index (IC)
  • Ulrichsweb (USA)