In accordance with regulations and requirements, the editorial department's website domain has been changed to arocmag.cn. The original domain (arocmag.com) has been redirecting to new domain since Jan. 1st, 2025.
See More
App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 6

Contrastive graph clustering based on multi-level feature fusion and enhancement

Li Zhiming1a,1b,1c,2
Wei Heping1a
Zhang Guangkang1a
You Dianlong1a,1b,1c,2
1. a. School of Information Science & Engineering, b. The Key Laboratory for Software Engineering of Hebei Province, c. The Key Laboratory for Computer Virtual Technology & System Integration of Hebei Province, Yanshan University, Qinhuangdao Hebei 066004, China
2. Shenzhen Research Institute, Yanshan University, Sehnzhen Guangdong 518063, China
DOI: 10.19734/j.issn.1001-3695.2024.11.0447

Abstract

The majority of existing contrastive graph clustering algorithms face the following issues: they ignore the low-level features and structural information extracted by shallow networks when generating node representation; the algorithms neither fully utilize high-order neighbor node information nor integrate confidence information with topological structure information to construct positive sample pairs. To address the above issues, this paper proposes a contrastive graph clustering algorithm based on multi-level feature fusion and enhancement. The algorithm first integrates node features extracted from different network layers to enrich the low-level structural information of nodes. It then aggregates node information through the local topological correlations and global semantic similarities between nodes to enhance the contextual constraint consistency of node representations. Finally, combining confidence information and topological structure information, the algorithm constructs more high-quality positive sample pairs to improve the consistency of intra-cluster representation. The experimental results show that CGCMFFE has excellent performance on four widely used clustering evaluation metrics. Theoretical analysis and experimental study underscore the crucial role of low-level node features, high-order neighbor node information, confidence, and topological structure information in the CGCMFFE algorithm, providing evidence for its superiority.

Foundation Support

国家自然科学基金面上项目(62276226)
河北中央引导地方项目(236Z7725G)
河北省重点研发计划项目(20375001D)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.11.0447
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 6

Publish History

[2025-03-10] Accepted Paper

Cite This Article

李志明, 魏贺萍, 张广康, 等. 基于多层特征融合与增强的对比图聚类 [J]. 计算机应用研究, 2025, 42 (6). (2025-03-10). https://doi.org/10.19734/j.issn.1001-3695.2024.11.0447. (Li Zhiming, Wei Heping, Zhang Guangkang, et al. Contrastive graph clustering based on multi-level feature fusion and enhancement [J]. Application Research of Computers, 2025, 42 (6). (2025-03-10). https://doi.org/10.19734/j.issn.1001-3695.2024.11.0447. )

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)