App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 5

Adaptive community detection algorithm based on weighted node affinity

Fu Lidong
Wu Jing
School of Computer Science and Technology, Xi'an University of Science and Technology, Xi'an 710699, China
DOI: 10.19734/j.issn.1001-3695.2025.10.0395

Abstract

Traditional community detection algorithms overly rely on first-order topological features and struggle to capture implicit community relationships in higher-order structures. To address this issue, this paper proposed an Adaptive Community Detection Algorithm based on Weighted Node Affinity (ACD-WNA) . First, the algorithm constructed a node affinity model that integrates edge weights with second-order relationships of common neighbors. Through differentiated weight assignment, the model overcame the limitations of existing similarity metrics and accurately extracted higher-order topological features from networks. Second, the paper designed a dual-constraint expansion mechanism that combines local structural similarity with global modularity gain. This mechanism effectively balanced community cohesiveness and overall partition quality while preventing community over-expansion. Finally, the paper introduced a global node reassignment strategy based on maximal modularity gain to dynamically optimize and correct community boundaries. Experiments on multiple real-world and synthetic networks demonstrated that ACD-WNA achieved excellent performance on key metrics such as NMI and cut_ratio. The algorithm consistently delivers high-quality community partitions with outstanding adaptability and robustness.

Foundation Support

国家自然科学基金资助项目(12071367)
陕西省自然科学基础研究计划基金资助项目(2023-JC-YB-517)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.10.0395
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 5

Publish History

[2026-01-20] Accepted Paper

Cite This Article

付立东, 吴静. 基于加权节点亲和度的自适应社区检测算法 [J]. 计算机应用研究, 2026, 43 (5). (2026-01-20). https://doi.org/10.19734/j.issn.1001-3695.2025.10.0395. (Fu Lidong, Wu Jing. Adaptive community detection algorithm based on weighted node affinity [J]. Application Research of Computers, 2026, 43 (5). (2026-01-20). https://doi.org/10.19734/j.issn.1001-3695.2025.10.0395. )

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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