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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 1
97-101

Network traffic prediction based on dynamic diffusion convolutional interaction graph neural network

Wang Jing1,2
Wen Xiaodong1
Wang Chunzhi1
1. School of Computer Science, Hubei University of Technology, Wuhan 430068, China
2. School of Computer Science, Wuhan University, Wuhan 430072, China
DOI: 10.19734/j.issn.1001-3695.2022.05.0255

Abstract

The existing network traffic prediction models have problems such as weak generalization ability and low prediction accuracy. To solve this problem, this paper proposed a prediction model combining dynamic diffusion convolution module and convolution interaction module. The dynamic diffusion convolution module could extract the complex spatial and dynamic characteristics of network traffic, while the convolution interaction module could capture the temporal characteristics of the traffic. The two organic combination could effectively predict the traffic in the network. This paper verified the effectiveness of the proposed dynamic diffusion convolutional interaction graph neural network(DDCIGNN) model by comparative experiments with other network traffic prediction models on the flow data of the US energy science network(ESnet). Experimental results show that the root mean square error(RMSE) of the DDCIGNN model is optimized by about 13.0% in the best case, which indicates that the model can perform better in network traffic prediction.

Foundation Support

国家自然科学基金资助项目(61772180)
湖北省重点研发计划资助项目(2020BHB004,2020BAB012)
湖北省自然科学基金面上类资助项目(2021CFB606)
湖北工业大学博士科研基金资助项目(BSQD2020062)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.05.0255
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 1
Section: Algorithm Research & Explore
Pages: 97-101
Serial Number: 1001-3695(2023)01-016-0097-05

Publish History

[2022-08-29] Accepted Paper
[2023-01-05] Printed Article

Cite This Article

王菁, 文晓东, 王春枝. 基于动态扩散卷积交互图神经网络的网络流量预测 [J]. 计算机应用研究, 2023, 40 (1): 97-101. (Wang Jing, Wen Xiaodong, Wang Chunzhi. Network traffic prediction based on dynamic diffusion convolutional interaction graph neural network [J]. Application Research of Computers, 2023, 40 (1): 97-101. )

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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