Semantic-Driven Community Detection in Complex Networks Using Fuzzy Logic and Multi-Criteria Decision Making
TL;DR
This study introduces a novel approach to community detection in complex networks by integrating fuzzy logic with multi-criteria decision-making techniques, and customizes the k-means clustering algorithm to accommodate small- and large-scale network structures.
Abstract
The study introduces a novel approach to community detection in complex networks by integrating fuzzy logic with multi-criteria decision-making techniques. Unlike traditional methods that rely primarily on topological metrics, the proposed approach incorporates semantic attributes to identify meaningful community structures. Fuzzy logic addresses the inherent uncertainty and ambiguity in processing these attributes, enabling a flexible detection process that is not dependent on network topology. To enhance scalability, this study customizes the k-means clustering algorithm to accommodate small- and large-scale network structures. Experimental results show that the proposed fuzzy logic-based approach achieves competitive performance compared with conventional algorithms. Additionally, the proposed approach demonstrates robustness by generating well-balanced communities with competitive execution times compared with the evaluated methods. These findings highlight the potential benefits of incorporating semantic attributes and fuzzy reasoning into community detection in complex networks.