Structural Analysis of Ladder Corona Graphs via Intuitionistic Fuzzy Cube Difference Labeling

Abstract

Intuitionistic fuzzy graph labeling is a useful approach for describing uncertainty based on membership (MS) and non-membership (NMS) values. This work studies Intuitionistic Fuzzy Cube Difference Labeling (IFCDL) on ladder-based graphs and corona products. Explicit labeling techniques are designed for ladder graphs, open ladder graphs, slanting ladder graphs, circular ladder graphs, and Mobius ladder graphs. It is demonstrated that these graphs permit intuitionistic fuzzy cube difference graphs (IFCDGs) by assigning distinct MS and NMS values within the range of [0, 1]. The suggested framework is adaptable to different graph products and complicated network models, with potential applications in decision-making, communication networks, and uncertainty-based systems.