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Fuzzy and neutrosophic modeling for link prediction in social networks

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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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Authors and Affiliations

  1. Thuyloi University, 175 Tay Son, Dong Da, Hanoi, Vietnam

    Tran Manh Tuan & Tran Thi Ngan

  2. Hung Yen University of Technology and Education, Hung Yen, Vietnam

    Pham Minh Chuan

  3. University of Southern Queensland, Toowoomba, 4300, Australia

    Mumtaz Ali

  4. Govind Ballabh Pant Engineering College, Phase-III, Okhla, New Delhi, India

    Mamta Mittal

  5. VNU Information Technology Institute, Vietnam National University, Hanoi, Vietnam

    Le Hoang Son

Authors
  1. Tran Manh Tuan
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  2. Pham Minh Chuan
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  3. Mumtaz Ali
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  4. Tran Thi Ngan
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  5. Mamta Mittal
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  6. Le Hoang Son
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Correspondence to Tran Manh Tuan or Le Hoang Son.

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Tuan, T.M., Chuan, P.M., Ali, M. et al. Fuzzy and neutrosophic modeling for link prediction in social networks. Evolving Systems 10, 629–634 (2019). https://doi.org/10.1007/s12530-018-9251-y

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  • DOI: https://doi.org/10.1007/s12530-018-9251-y

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