TY  - JOUR
AU  - Alharbi, Rehab
AU  - Arshad, Hibba
AU  - Javaid, Imran
AU  - Koam, Ali. N. A.
AU  - Haider, Azeem
PY  - 2025
TI  - Distance-based granular computing in networks modeled by intersection graphs
JO  - AIMS Mathematics
SP  - 10528
EP  - 10553
VL  - 10
IS  - 5
AB  - Networks are commonly represented as graphs, where vertices denote entities and edges capture relationships based on shared attributes. Granulation of a network is important for the structural analysis and understanding of its underlying patterns. In this paper, we introduce a distance-based granular computing framework for analyzing networks modeled by intersection graphs. We define these networks as information systems and investigate their granular structures using a distance-based representation. Based on the concepts of indiscernibility between two vertices using the distance from a set, we study indiscernibility partitions on the vertex set. Using the concept of discernibility between vertices, we define the distance-based discernibility matrix and explore its properties. We identify all minimal resolving sets using the discernibility matrix. Furthermore, using the proposed method, we study a transportation network for urban traffic planning.
UR  - https://doi.org/10.3934/math.2025479
DO  - 10.3934/math.2025479