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App. Res. of Comp. Printed Article
Vol. 43, 2026 No. 4
972-984

Overview on node classification in multilayer networks based on graph neural network

Chen Kexin
Ding Cangfeng
Zhu Ye
Cao Bohao
College of Mathematics and Computer Science, Yan'an University, Yan'an Shaanxi 716000, China
DOI: 10.19734/j.issn.1001-3695.2025.09.0309

Abstract

Multilayer network node classification is a critical task in complex network research. It aims to identify and classify nodes within a network to uncover their intrinsic multidimensional structure and semantic characteristics. GNN, as a framework capable of directly process graph-structured data, can effectively capture the underlying patterns of nodes and edges, as well as more profound semantic features, through message-passing mechanisms. In node classification tasks, GNN exploits graph structure information and node features by aggregating information from neighboring nodes and propagating messages. Compared to traditional node classification methods, GNN learns node feature representations adaptively, reducing reliance on manual feature engineering and enhancing classification accuracy. This paper categorized and reviewed recent developments in GNN-based multilayer network node classification methods. Firstly, it outlined the core concepts of multilayer networks and GNN, and provided definitions and characteristics of various multilayer network models. Then, it comprehensively summarized the latest advancements in GNN-based multilayer network node classification methods, and categorized them into three paradigms based on the learning approach: semi-supervised, unsupervised, and self-supervised learning. This paper also analyzed the performance of these methods in downstream tasks, such as social networks, academic networks, and E-commerce platforms. Finally, it provided a thorough summary of existing research, discussed the limitations of current methods, and suggested potential directions for future research.

Foundation Support

国家自然科学基金资助项目(62262067)
陕西省人才项目(YAU202213065,CXY202107)
延安大学十四五重大科研项目(2021ZCQ012)
延安大学基金资助项目(YCX2024049,YDJG23-27,D2022034)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.09.0309
Publish at: Application Research of Computers Printed Article, Vol. 43, 2026 No. 4
Section: Survey
Pages: 972-984
Serial Number: 1001-3695(2026)04-002-0972-13

Publish History

[2025-12-19] Accepted Paper
[2026-04-05] Printed Article

Cite This Article

陈科鑫, 丁苍峰, 朱叶, 等. 面向图神经网络的多层网络节点分类研究综述 [J]. 计算机应用研究, 2026, 43 (4): 972-984. (Chen Kexin, Ding Cangfeng, Zhu Ye, et al. Overview on node classification in multilayer networks based on graph neural network [J]. Application Research of Computers, 2026, 43 (4): 972-984. )

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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