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Article | Open Access

Link Prediction in Co-Authorship Network under Fuzziness and Application in Biomedical Analysis

Kousik Das1, Ananta Maity2, Kajal De1,3, Sukumar Mondal2, Sovan Samanta4,5( ), Tofigh Allahviranloo5
Department of Mathematics, Netaji Subhas Open University, Kolkata 700091, India
Department of Mathematics, Raja Narendra Lal Khan Women’s College, Midnapore 721102, India
Diamond Harbour Women’s University, Kolkata 743368, India
Department of Technical Sciences, Western Caspian University, Baku 1001, Azerbaijan
Research Center of Performance and Productivity Analysis, Istinye University, Istanbul 34460, Türkiye
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Abstract

We aim to predict links in fuzzy social networks, where the existing methods based on common neighbors of two nodes are not effective. These methods are local measures that only work when the shortest distance between two nodes is less than or equal to two. Our method can handle cases where the shortest distance is between three and five. We define the concepts of link strength and path strength in a network and propose an algorithm for predicting links. We illustrate our method with a numerical example in a co-authorship network and discuss application areas in biomedical.

Keywords

social networklink predictionpath

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Fuzzy Information and Engineering
Volume 16 Issue 2,
June 2024
Pages 155-174
DOI: 10.26599/FIE.2024.9270039
Cite this article:
Das K, Maity A, De K, et al. Link Prediction in Co-Authorship Network under Fuzziness and Application in Biomedical Analysis. Fuzzy Information and Engineering, 2024, 16(2): 155-174. https://doi.org/10.26599/FIE.2024.9270039
Part of a topical collection:
Advances in Fuzzy Computational Intelligence for Biomedical Applications

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Received: 30 November 2023
Revised: 09 May 2024
Accepted: 25 May 2024
Published: 30 June 2024
© The Author(s) 2024. Published by Tsinghua University Press.

This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
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