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Special Topics in Graph Neural Network
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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

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.

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