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Special Topics in Contrastive and Non-contrastive Learning
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 9
2611-2619

Heterogeneous graph embedding based on cross-view prototype non-contrastive learning

Zhang Min
Yang Yuqing
He Yanting
Shi Chenhui
School of Computer Science & Technology, Taiyuan University of Science & Technology, Taiyuan 030024, China
DOI: 10.19734/j.issn.1001-3695.2024.01.0016

Abstract

Heterogeneous graph embedding models based on non-contrastive learning(NCL) do not rely on negative sampling to learn the intrinsic features and patterns, which may cause the model fail to efficiently learn the differences between vertexes. This paper proposed a heterogeneous graph embedding model based on cross-view prototype non-contrastive learning(XP-NCL), which learnt better node representations for downstream tasks by finding additional positive samples with more contextual information, and reconsidered the similarity between positive samples. The model firstly designed a tree structure based on random walks in heterogeneous graph. This directed filtering tree(DFT) about positive samples contained rich neighboring and semantic information by filtering out random walk paths that satisfied local structural constraints. Secondly, to achieve the alignment of similar samples in terms of numerical and quantitative from multiple dimensions, XP-NCL defined the cross-view prototype index(ISDR) and peak operator based on the characteristics of heterogeneous graphs. Furthermore, the model trained using stop-gradient updating. Finally, experiments verify the classification and clustering performance of the node on ACM, DBLP and freebase datasets, and the results show that even without the negative samples, the XP-NCL representation can achieve superior performance in many cases compared to other homogeneous and heterogeneous graph baselines.

Foundation Support

国家自然科学基金资助项目(U1931209)
山西省科技合作交流专项区域合作项目(202204041101037,202204041101033)
太原科技大学研究生教育创新项目(BY2023015)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.01.0016
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 9
Section: Special Topics in Contrastive and Non-contrastive Learning
Pages: 2611-2619
Serial Number: 1001-3695(2024)09-007-2611-09

Publish History

[2024-04-02] Accepted Paper
[2024-09-05] Printed Article

Cite This Article

张敏, 杨雨晴, 贺艳婷, 等. 基于跨视图原型非对比学习的异构图嵌入模型 [J]. 计算机应用研究, 2024, 41 (9): 2611-2619. (Zhang Min, Yang Yuqing, He Yanting, et al. Heterogeneous graph embedding based on cross-view prototype non-contrastive learning [J]. Application Research of Computers, 2024, 41 (9): 2611-2619. )

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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