Large-scale consensus in incomplete social network with non-cooperative behaviors and dimension reduction
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F25%3A50021796" target="_blank" >RIV/62690094:18450/25:50021796 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S0020025524014774?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0020025524014774?via%3Dihub</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.ins.2024.121563" target="_blank" >10.1016/j.ins.2024.121563</a>
Alternative languages
Result language
angličtina
Original language name
Large-scale consensus in incomplete social network with non-cooperative behaviors and dimension reduction
Original language description
Obtaining a consensus solution is a formidable challenge for large-scale decision-making (LSDM) in a social network (SN). The reaching of large-scale consensus in SN is hindered by serious difficulties, including complex decision information, incomplete social relations, a multitude of decision-makers (DMs), and non-cooperative behaviors. This paper introduces a novel three-stage consensus framework that systematically addresses these challenges by data preprocessing, dimension reduction, and optimization modeling. Firstly, the cloud model is applied to convert the probabilistic linguistic information into numerical information, facilitating computational analysis. Meanwhile, an improved t-norm trust propagation method that incorporates the impact of opinion similarity is developed, ensuring the completeness of SN. Secondly, an improved Louvain algorithm is designed to divide large group into cohesive subgroups, enhancing the manageability of LSDM. On this basis, a three-stage consensus optimization that considers non-cooperative behaviors is proposed, which boasts threefold benefits: (i) Assures the synchronous achievement of local and global consensus. (ii) Implements self-adaptive management mechanism of non-cooperative behaviors. (iii) Provides acceptable adjusted opinions for subgroups and DMs. Finally, detailed numerical experiments and comparative analyses are given to demonstrate the effectiveness of the proposed method. © 2024 Elsevier Inc.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Information sciences
ISSN
0020-0255
e-ISSN
1872-6291
Volume of the periodical
690
Issue of the periodical within the volume
February
Country of publishing house
US - UNITED STATES
Number of pages
26
Pages from-to
"Article number: 121563"
UT code for WoS article
001369180100001
EID of the result in the Scopus database
2-s2.0-85206885135