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A Study on Linguistic Z-Graph and Its Application in Social Networks

Author

Listed:
  • Rupkumar Mahapatra

    (Department of Applied Mathematics, Vidyasagar University, Midnapore 721102, West Bengal, India)

  • Sovan Samanta

    (Department of Technical Sciences, Western Caspian University, Baku AZ1001, Azerbaijan
    Department of Mathematics, Tamralipta Mahavidyalaya, Tamluk 721636, West Bengal, India
    Research Center of Performance and Productivity Analysis, Istinye University, Istanbul 34396, Türkiye
    Department of Technical Sciences, Algebra University, Gradiscanska 24, 10000 Zagreb, Croatia)

  • Madhumangal Pal

    (Department of Applied Mathematics, Vidyasagar University, Midnapore 721102, West Bengal, India
    Department of Mathematics and Innovation, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai 602105, Tamilnadu, India)

  • Tofigh Allahviranloo

    (Research Center of Performance and Productivity Analysis, Istinye University, Istanbul 34396, Türkiye
    Quantum Technologies Research Center (QTRC), Science and Research Branch, Islamic Azad University, Tehran 1477893855, Iran)

  • Antonios Kalampakas

    (College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait)

Abstract

This paper presents a comprehensive study of the linguistic Z-graph, which is a novel framework designed to analyze linguistic structures within social networks. By integrating concepts from graph theory and linguistics, the linguistic Z-graph provides a detailed understanding of language dynamics in online communities. This study highlights the practical applications of linguistic Z-graphs in identifying central nodes within social networks, which are crucial for online businesses in market capture and information dissemination. Traditional methods for identifying central nodes rely on direct connections, but social network connections often exhibit uncertainty. This paper focuses on using fuzzy theory, particularly linguistic Z-graphs, to address this uncertainty, offering more detailed insights compared to fuzzy graphs. Our study introduces a new centrality measure using linguistic Z-graphs, enhancing our understanding of social network structures.

Suggested Citation

  • Rupkumar Mahapatra & Sovan Samanta & Madhumangal Pal & Tofigh Allahviranloo & Antonios Kalampakas, 2024. "A Study on Linguistic Z-Graph and Its Application in Social Networks," Mathematics, MDPI, vol. 12(18), pages 1-24, September.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:18:p:2898-:d:1479847
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    References listed on IDEAS

    as
    1. Rupkumar Mahapatra & Sovan Samanta & Madhumangal Pal & Anibal Coronel, 2022. "Edge Colouring of Neutrosophic Graphs and Its Application in Detection of Phishing Website," Discrete Dynamics in Nature and Society, Hindawi, vol. 2022, pages 1-8, July.
    2. Pierre Hansen & Julio Kuplinsky & Dominique Werra, 1997. "Mixed graph colorings," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 45(1), pages 145-160, February.
    3. Chao Song & Jian-Qiang Wang & Jun-Bo Li, 2020. "New Framework for Quality Function Deployment Using Linguistic Z-Numbers," Mathematics, MDPI, vol. 8(2), pages 1-20, February.
    4. Ma, Ling-ling & Ma, Chuang & Zhang, Hai-Feng & Wang, Bing-Hong, 2016. "Identifying influential spreaders in complex networks based on gravity formula," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 205-212.
    5. Amir Hosein Mahmoodi & Seyed Jafar Sadjadi & Soheil Sadi-Nezhad & Roya Soltani & Farzad Movahedi Sobhani, 2020. "Linguistic Z-number weighted averaging operators and their application to portfolio selection problem," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-34, January.
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