@article{Alharbi2025, 
author = {Rehab Alharbi and Hibba Arshad and Imran Javaid and Ali. N. A. Koam and Azeem Haider},
title = {Distance-based granular computing in networks modeled by intersection graphs},
year = {2025},
journal = {AIMS Mathematics},
volume = {10},
number = {5},
pages = {10528-10553},
keywords = {granular computing, network, intersection graphs, rough set, resolving set, reduct, discernibility matrix},
url = {https://www.sciopen.com/article/10.3934/math.2025479},
doi = {10.3934/math.2025479},
abstract = {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.}
}