Algorithm Research & Explore
|
App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 5
1389-1395

Signed bipartite graph neural network enhanced by potential group assignment and contrast learning

Wu Yong1a
Tong Xin2
Gao Guandong1a,1b
Ma Guofu1a
1. a. Dept. of Information Management, b. The Centre of Data Science & Intelligent Correction Technology, The National Police University for Criminal Justice, Baoding Hebei 071001, China
2. School of Information Technology & Cyber Security, People's Public Security University of China, Beijing 100038, China
DOI: 10.19734/j.issn.1001-3695.2022.09.0497

Abstract

To address the problems of node heterogeneity and inapplicability of triangular form balance theory in signed bipartite network modeling, this paper proposed a signed bipartite graph neural network enhanced by potential group assignment and contrast learning, which could extract the display and implicit information fully through complementing each other with homogeneous and heterogeneous spaces. In the homogeneous space, this paper treated nodes as a combination of multiple learnable potential groups, and then mined information among nodes by training automatically. In the heterogeneous space, this paper adopted the attention aggregators with directions to aggregate information of neighbors, and then used the contrast learning for network reconstruction based on mutual information to guide the aggregation process to obtain more expressive node representations. This paper performed comparative experiments with a variety of related models on the signed link prediction task. Experimental results show that it can obtain optimal values for 12 of the 16 evaluation results obtained using four evaluation metrics on four real datasets, which verifies the effectiveness of the proposed model.

Foundation Support

教育部第二批新工科研究与实践资助项目(E-GKRWJC20202905)
国家社会科学基金重点项目(20AZD114)
河北省社会科学基金资助项目(HB21ZZ002)
河南省重点研发与推广项目(212102210165)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.09.0497
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 5
Section: Algorithm Research & Explore
Pages: 1389-1395
Serial Number: 1001-3695(2023)05-016-1389-07

Publish History

[2023-01-04] Accepted Paper
[2023-05-05] Printed Article

Cite This Article

吴勇, 仝鑫, 高冠东, 等. 基于潜在组分配及对比学习增强的符号二值图神经网络 [J]. 计算机应用研究, 2023, 40 (5): 1389-1395. (Wu Yong, Tong Xin, Gao Guandong, et al. Signed bipartite graph neural network enhanced by potential group assignment and contrast learning [J]. Application Research of Computers, 2023, 40 (5): 1389-1395. )

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.

Indexed & Evaluation

  • The Second National Periodical Award 100 Key Journals
  • Double Effect Journal of China Journal Formation
  • the Core Journal of China (Peking University 2023 Edition)
  • the Core Journal for Science
  • Chinese Science Citation Database (CSCD) Source Journals
  • RCCSE Chinese Core Academic Journals
  • Journal of China Computer Federation
  • 2020-2022 The World Journal Clout Index (WJCI) Report of Scientific and Technological Periodicals
  • Full-text Source Journal of China Science and Technology Periodicals Database
  • Source Journal of China Academic Journals Comprehensive Evaluation Database
  • Source Journals of China Academic Journals (CD-ROM Version), China Journal Network
  • 2017-2019 China Outstanding Academic Journals with International Influence (Natural Science and Engineering Technology)
  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
  • Source Journal of China Engineering Technology Electronic Information Network and Electronic Technology Literature Database
  • Source Journal of British Science Digest (INSPEC)
  • Japan Science and Technology Agency (JST) Source Journal
  • Russian Journal of Abstracts (AJ, VINITI) Source Journals
  • Full-text Journal of EBSCO, USA
  • Cambridge Scientific Abstracts (Natural Sciences) (CSA(NS)) core journals
  • Poland Copernicus Index (IC)
  • Ulrichsweb (USA)