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Research Progress of Complex Network Modeling Methods Based on Uncertainty Theory

Author

Listed:
  • Jing Wang

    (College of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China
    Basic Teaching Department, Tangshan University, Tangshan 063210, China)

  • Jing Wang

    (College of Science, North China University of Science and Technology, Tangshan 063210, China)

  • Jingfeng Guo

    (College of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China
    Hebei Key Laboratory of Data Science and Application, North China University of Science and Technology, Tangshan 063210, China
    The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Qinhuangdao 066004, China)

  • Liya Wang

    (College of Science, North China University of Science and Technology, Tangshan 063210, China
    Hebei Key Laboratory of Data Science and Application, North China University of Science and Technology, Tangshan 063210, China
    The Key Laboratory of Engineering Computing in Tangshan City, North China University of Science and Technology, Tangshan 063210, China
    Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes, North China University of Science and Technology, Tangshan 063210, China)

  • Chunying Zhang

    (College of Science, North China University of Science and Technology, Tangshan 063210, China
    Hebei Key Laboratory of Data Science and Application, North China University of Science and Technology, Tangshan 063210, China
    The Key Laboratory of Engineering Computing in Tangshan City, North China University of Science and Technology, Tangshan 063210, China
    Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes, North China University of Science and Technology, Tangshan 063210, China)

  • Bin Liu

    (Big Data and Social Computing Research Center, Hebei University of Science and Technology, Shijiazhuang 050018, China)

Abstract

A complex network in reality contains a large amount of information, but some information cannot be obtained accurately or is missing due to various reasons. An uncertain complex network is an effective mathematical model to deal with this problem, but its related research is still in its infancy. In order to facilitate the research into uncertainty theory in complex network modeling, this paper summarizes and analyzes the research hotspots of set pair analysis, rough set theory and fuzzy set theory in complex network modeling. This paper firstly introduces three kinds of uncertainty theories: the basic definition of set pair analysis, rough sets and fuzzy sets, as well as their basic theory of modeling in complex networks. Secondly, we aim at the three uncertainty theories and the establishment of specific models. The latest research progress in complex networks is reviewed, and the main application fields of the three uncertainty theories are discussed, respectively: community discovery, link prediction, influence maximization and decision-making problems. Finally, the prospect of the modeling and development of uncertain complex networks is put forward.

Suggested Citation

  • Jing Wang & Jing Wang & Jingfeng Guo & Liya Wang & Chunying Zhang & Bin Liu, 2023. "Research Progress of Complex Network Modeling Methods Based on Uncertainty Theory," Mathematics, MDPI, vol. 11(5), pages 1-27, March.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:5:p:1212-:d:1084995
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    References listed on IDEAS

    as
    1. Zhang, Shihua & Wang, Rui-Sheng & Zhang, Xiang-Sun, 2007. "Identification of overlapping community structure in complex networks using fuzzy c-means clustering," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 374(1), pages 483-490.
    2. Liyan Zhang & Jingfeng Guo & Jiazheng Wang & Jing Wang & Shanshan Li & Chunying Zhang, 2022. "Hypergraph and Uncertain Hypergraph Representation Learning Theory and Methods," Mathematics, MDPI, vol. 10(11), pages 1-22, June.
    3. Jiang, Yawen & Jia, Caiyan & Yu, Jian, 2013. "An efficient community detection method based on rank centrality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(9), pages 2182-2194.
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