Fuzzy and neutrosophic modeling for link prediction in social networks

Tuan, Tran Manh and Chuan, Pham Minh and Ali, Mumtaz ORCID: https://orcid.org/0000-0002-6975-5159 and Ngan, Tran Thi and Mittal, Mamta and Son, Le Hoang (2019) Fuzzy and neutrosophic modeling for link prediction in social networks. Evolving Systems, 10. pp. 629-634. ISSN 1868-6478


Abstract

Some new similarity measures for link prediction based on fuzzy and neutrosophic environments are proposed. It aims to determine possible association between two objects in a social network represented by a graph including nodes and edges. It is widely used in various domains such as in the co-authorship network and protein-interaction systems. Similarity measure is an important tool for such the determination. Herein, some new fuzzy and neutrosophic measures are proposed accompanied with mathematical properties. The validation on the co-authorship network datasets demonstrates the efficiency of the proposed method.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Files associated with this item cannot be displayed due to copyright restrictions.
Faculty/School / Institute/Centre: Historic - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sep 2019 - 31 Dec 2021)
Faculty/School / Institute/Centre: Historic - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sep 2019 - 31 Dec 2021)
Date Deposited: 08 Jun 2022 22:57
Last Modified: 08 Aug 2022 04:55
Uncontrolled Keywords: Co-authorship network, Link prediction, Social networks, Fuzzy similarity measures, Neutrosophic measures
Fields of Research (2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080105 Expert Systems
08 Information and Computing Sciences > 0802 Computation Theory and Mathematics > 080201 Analysis of Algorithms and Complexity
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4613 Theory of computation > 461399 Theory of computation not elsewhere classified
46 INFORMATION AND COMPUTING SCIENCES > 4605 Data management and data science > 460510 Recommender systems
Identification Number or DOI: https://doi.org/10.1007/s12530-018-9251-y
URI: http://eprints.usq.edu.au/id/eprint/48914

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