Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 37, 2020 No. 7
1999-2005

Using mutual exclusion strategy to optimize node prediction in bipartite network

Fan Chunlong1,2
Fan Dongwan2
Xu Li2
He Yufeng2
1. Large-scale Distributed System Laboratory in Liaoning Province, Shenyang 110136, China
2. College of Computer Science, Shenyang Aerospace University, Shenyang 110136, China
DOI: 10.19734/j.issn.1001-3695.2018.12.0938

Abstract

Network node prediction research currently focuses on the prediction of source nodes and hidden nodes, but lacks research on prediction of new nodes. This paper took the relational network of papers and keywords as the research object, used keyword combination to predict the emergence of new papers, and carried out the prediction research of new nodes. First, this essay projected and weighted the paper-keyword bipartite network into a keyword relational network, and then used the possibility of keyword combination to predict the emergence of new papers in the future. There are two aspects to consider to calculate this possibility. One is similarity, which indicates the tendency of keywords to co-occur; and the other is mutual exclusion, which describes the tendency of keywords to exclude each other. For example, two keywords with a high degree of concord rarely appear at the same time. Collected the papers and keywords information of the journal to construct the dataset, it verifies the proposed new paper prediction algorithm, and compared with the existing algorithms. The results show that the node prediction algorithm proposed has better prediction effect.

Foundation Support

国家自然科学基金资助项目(61303016)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.12.0938
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 7
Section: Algorithm Research & Explore
Pages: 1999-2005
Serial Number: 1001-3695(2020)07-016-1999-07

Publish History

[2020-07-05] Printed Article

Cite This Article

范纯龙, 范东皖, 许莉, 等. 利用互斥策略优化二分网络节点预测 [J]. 计算机应用研究, 2020, 37 (7): 1999-2005. (Fan Chunlong, Fan Dongwan, Xu Li, et al. Using mutual exclusion strategy to optimize node prediction in bipartite network [J]. Application Research of Computers, 2020, 37 (7): 1999-2005. )

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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