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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 7

Self-supervised heterogeneous graph embedding algorithm based on higher-order neighborhood information interaction

Dong Jiahao1a
Wu Yongliang1a,1b,1c
Wang Zhiqiang2
Han Xu3
1. a. School of Information Science & Technology, b. Hebei Key Laboratory of Electromagnetic Environmental Effects & Information Processing, c. Shijiazhuang Key Laboratory of Artificial Intelligence, Shijiazhuang Tiedao University, Shijiazhuang 050043, China
2. Institute of Applied Mathematics, Hebei Academy of Sciences, Shijiazhuang 050081, China
3. Software College, Shenyang Normal University, Shenyang 110034, China
DOI: 10.19734/j.issn.1001-3695.2024.11.0493

Abstract

To address the issue that current self-supervised neural network algorithms do not consider the impact of high-order node information when obtaining neighborhood node weights, this paper proposes a self-supervised heterogeneous graph embedding method based on high-order neighborhood information interaction, SSHGEA-HNI. It enhances local optimization capabilities and model performance by adding a feedforward fully connected layer in the attention mechanism to capture high-order neighborhood node features. The model consists of a label generation module and an embedding learning module. The label generation module produces pseudo-labels for nodes through label propagation, which serve as supervisory signals to guide the embedding generation module to produce embeddings. The embedding learning module generates embeddings and attention coefficients through the attention mechanism based on high-order neighborhood information interaction, with the attention coefficients used to guide the label generation module to produce pseudo-labels. In each iteration, the two modules share node attention coefficients, promoting mutual utilization and enhancement between the two modules. Experiments were conducted on four real heterogeneous graph datasets, with improvements observed in the clustering and classification tasks of most datasets. The experimental results demonstrate that the model can effectively utilize high-order node information.

Foundation Support

国家自然科学基金资助项目(62106157)
河北省自然科学基金资助项目(F2024210005)
石家庄铁道大学高等教育教学研究项目(Y2023-13)
河北省科学院科技计划项目(24606)
河北省高等教育学会"十四五"规划项目(GJXH2024-079)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.11.0493
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 7

Publish History

[2025-03-14] Accepted Paper

Cite This Article

董家浩, 武永亮, 王志强, 等. 基于高阶邻域信息交互的自监督异质图嵌入算法 [J]. 计算机应用研究, 2025, 42 (7). (2025-03-14). https://doi.org/10.19734/j.issn.1001-3695.2024.11.0493. (Dong Jiahao, Wu Yongliang, Wang Zhiqiang, et al. Self-supervised heterogeneous graph embedding algorithm based on higher-order neighborhood information interaction [J]. Application Research of Computers, 2025, 42 (7). (2025-03-14). https://doi.org/10.19734/j.issn.1001-3695.2024.11.0493. )

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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