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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 42, 2025 No. 2
406-412

Heterogeneous hypernetwork representation learning with dual-end weight constraints

Chen Yijiana,b
Zhu Yua,b
Wang Xiaoyinga,b
Huang Jianqianga,b
Cao Tengfeia,b
Wang Weia,b
a. School of Computer Technology & Application, b. Qinghai Provincial Laboratory for Intelligent Computing & Application, Qinghai University, Xining 810016, China
DOI: 10.19734/j.issn.1001-3695.2024.07.0279

Abstract

Different from traditional networks, the hypernetworks possess complex higher-order tuple relationships, which fail to be captured by most existing hypernetwork representation learning methods effectively. To address this issue and better capture complex higher-order tuple relationships, this paper proposed a heterogeneous hypernetwork representation learning method with dual-end weight constraints abbreviated as HRDC. Firstly, this method proposed a hyperedge multi-source random walk fusion algorithm that incorporated the hyperedges into random walk node sequences based on the hyperpaths. Secondly, inspired by the knowledge representation learning model TransE, this method introduced hyperedge perceptron model and hyper-gram model to carry out weighted fusion, so as to capture complex higher-order tuple relationships in the hypernetworks. Finally, the experiments on four real-world hypernetwork datasets demonstrate that for link prediction tasks, the performance of this method is almost superior to all baseline methods. As for hypernetwork reconstruction tasks, on the GPS dataset, the performance of this method surpasses all baseline methods. On the drug dataset, when the hyperedge reconstruction ratio exceeds 0.3, the performance of this method outperforms all baseline methods. In summary, the proposed method can effectively capture complex higher-order tuple relationships in the hypernetworks.

Foundation Support

国家自然科学基金资助项目(62166032,62162053)
青海省自然科学基金资助项目(2022-ZJ-961Q)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.07.0279
Publish at: Application Research of Computers Printed Article, Vol. 42, 2025 No. 2
Section: Algorithm Research & Explore
Pages: 406-412
Serial Number: 1001-3695(2025)02-011-0406-07

Publish History

[2025-02-05] Printed Article

Cite This Article

陈毅艰, 朱宇, 王晓英, 等. 基于双端权重约束的异质超网络表示学习 [J]. 计算机应用研究, 2025, 42 (2): 406-412. (Chen Yijian, Zhu Yu, Wang Xiaoying, et al. Heterogeneous hypernetwork representation learning with dual-end weight constraints [J]. Application Research of Computers, 2025, 42 (2): 406-412. )

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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