Link prediction in m-polar fuzzy environment and its application

Link prediction plays a vital role in the growth and sustainability of social and telecom networks by facilitating the formation of new connections and enhancing user engagement. In this context, m-polar fuzzy graphs provide a flexible framework for modeling complex networks with multiple interacting attributes. This study introduces an RMS index-based approach for link prediction in m-polar fuzzy graphs, where multi-polar membership degrees effectively capture the diverse influence patterns among non-adjacent nodes. The proposed index reflects the multidimensional nature of network interactions and supports a comprehensive analysis of structural dynamics. Theoretical properties of the method are examined to demonstrate its robustness. Practical applicability is illustrated through real-world scenarios such as matrimonial networking platforms, where multiple simultaneous influences are essential for meaningful relationship formation.