Granular Rough–Fuzzy Similarity for InterpretableLink Prediction

Authors

  • Swasti Hazra Kundu * Natural Science Research Centre of Belda College under Vidyasagar University, Paschim Medinipur, India. Department of Mathematics, Ramnagar College, Purba Medinipur, India. https://orcid.org/0009-0004-6752-7103
  • Kalyani Maity Das Department of Mathematics, Belda College, Paschim Medinipur, India.

https://doi.org/10.48314/tsc.v1i3.61

Abstract

An interpretable link prediction framework is presented by combining rough sets, fuzzy reliability, and information granules. The proposed method assigns different importance to common neighbors instead of treating every witness equally.
Lower and upper rough approximations are used to represent certain and possible evidence. A normalized rough–fuzzy similarity score is obtained from their weighted combination. A rough–fuzzy Jaccard coefficient is also introduced to provide a normalized similarity measure within the same framework.
The proposed measures are proved to satisfy symmetry, boundedness, monotonicity, partition refinement, and reliability stability. A certified ranking rule is further established to identify stable link ordering under refinement.
A computational illustration demonstrates that candidate pairs with the same number of common neighbors can receive different similarity scores because witness reliability and granular structure are considered. The framework provides a transparent mathematical alternative for interpretable link prediction in attributed networks.

Keywords:

Rough set, Fuzzy neighborhood, Granular computing, Link prediction, Common neighbor, Partition refinement

References

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Published

2025-09-09

Issue

Vol. 1 No. 3 (2025): Transactions on Soft Computing

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Articles

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How to Cite

Kundu, S. H., & Das, K. M. (2025). Granular Rough–Fuzzy Similarity for InterpretableLink Prediction. Transactions on Soft Computing , 1(3), 168-182. https://doi.org/10.48314/tsc.v1i3.61