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
Special Topics in Graph Neural Network
|
App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 10
2926-2931

AGCFN: multiplex network community detection model based on graph neural network

Chen Long1a
Zhang Zhenyu1b
Li Xiaoming2
Bai Hongpeng3
1. a. College of Software, b. College of Information Science & Engineering, Xinjiang University, rümqi 830000, China
2. College of International Business, Zhejiang Yuexiu University, Shaoxing Zhejiang 312000, China
3. College of Intelligence & Computing, Tianjin University, Tianjin 300000, China
DOI: 10.19734/j.issn.1001-3695.2024.03.0056

Abstract

Multiplex network community detection methods based on graph neural network face two main challenges. Firstly, how to effectively utilize the node content information of multiplex network; and secondly, how to effectively utilize the interlayer relationships in multiplex networks. Therefore, this paper proposed the multiplex network community detection model AGCFN. Firstly, the autoencoder independently extracted the node content information of each network layer and passed the extracted node content information to the graph autoencoder for fusing the node content information of the current network layer with the topology information through the transfer operator to obtain the representation of each node of the current network layer, which made full use of the node content information of the network and the topology information of the network. The modularity maximization module and graph decoder optimized the obtained node representation. Secondly, the multilayer information fusion module fused the node representations extracted from each network layer to obtain a comprehensive representation of each node. Finally, the model under went training, and it achieved community detection results through a self-training mechanism. Comparison with six models on three datasets demonstrate improvements in both ACC and NMI evaluation metrics, thereby validating the effectiveness of AGCFN.

Foundation Support

国家自然科学基金资助项目(62272311)
国家重点研发计划资助项目(2018YFC0831005)
中国天津经济技术开发区科技支撑计划资助项目(STCKJ2020-WRJ)
中国新疆建设兵团第十二师财务科技项目(SR202103)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.03.0056
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 10
Section: Special Topics in Graph Neural Network
Pages: 2926-2931
Serial Number: 1001-3695(2024)10-007-2926-06

Publish History

[2024-07-05] Accepted Paper
[2024-10-05] Printed Article

Cite This Article

陈龙, 张振宇, 李晓明, 等. AGCFN:基于图神经网络多层网络社团检测模型 [J]. 计算机应用研究, 2024, 41 (10): 2926-2931. (Chen Long, Zhang Zhenyu, Li Xiaoming, et al. AGCFN: multiplex network community detection model based on graph neural network [J]. Application Research of Computers, 2024, 41 (10): 2926-2931. )

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)