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App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 3

Graph neural network for community detection via dynamic adjacency fusion and channel mixing

Ai Jun
Xiang Qian
Su Zhan
Xiao Chenxi
School of Optoelectronic Information and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
DOI: 10.19734/j.issn.1001-3695.2025.07.0271

Abstract

Dynamic community detection has become a critical task in graph representation learning due to the dynamic evolution of graph data in social networks and e-commerce platforms. Existing methods often model graph evolution using a unified time decay mechanism, struggling to characterize heterogeneous temporal behaviors. Moreover, they insufficiently model channel-wise feature interactions, thus limiting the balance between expressiveness and computational efficiency. To address these issues, a novel dynamic graph learning framework, the Temporal-Channel Graph Attention Network (TC-GAT) , was developed. The TC-GAT framework integrated a dynamic adjacency fusion (DAF) module into a graph attention network (GAT) backbone. The DAF module achieves multi-stage adjacency information fusion through node-adaptive temporal weighting, which effectively characterizes diverse evolutionary behaviors. Furthermore, a graph channel mixer (GCM) was introduced to model deep interactions between channels in a lightweight manner, substantially enhancing node representation capabilities. Experimental results on multiple real-world dynamic graph datasets show that TC-GAT significantly outperforms mainstream models in key metrics such as accuracy, F1 score, and AUC, while also demonstrating high training efficiency. These findings confirm that collaboratively modeling spatiotemporal evolution and channel interactions improves the overall performance of dynamic graph analysis.

Foundation Support

国家自然科学基金资助项目(61803264)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.07.0271
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 3

Publish History

[2025-11-18] Accepted Paper

Cite This Article

艾均, 向潜, 苏湛, 等. 基于动态邻接融合与通道混合的图神经网络社团检测方法 [J]. 计算机应用研究, 2026, 43 (3). (2025-11-18). https://doi.org/10.19734/j.issn.1001-3695.2025.07.0271. (Ai Jun, Xiang Qian, Su Zhan, et al. Graph neural network for community detection via dynamic adjacency fusion and channel mixing [J]. Application Research of Computers, 2026, 43 (3). (2025-11-18). https://doi.org/10.19734/j.issn.1001-3695.2025.07.0271. )

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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