Analysis of community groups in large dynamic social network graphs through fuzzy computation

This study presents a fuzzy computing approach for dynamic community detection, termed Fuzzy Time-variant Community Groups (FTCG), utilizing fuzzy weighting techniques to track evolving network structures over time. The methodology was validated on a small 5-node graph and applied to large-scale datasets, including Amazon product networks, Bitcoin transactions, and Cellular Phone Network data. Two novel link-weighting techniques were introduced to enhance the detection of temporal community changes, while a Fuzzy Modularity measure was proposed to evaluate community quality. The impact of varying threshold values was analyzed, demonstrating how different thresholds influence community detection outcomes. Experimental results confirm the approach's effectiveness in capturing network dynamics, particularly in the Bitcoin and Cellular datasets, proving its robustness in Social Network Analysis (SNA) and its potential for informed decision-making in evolving systems.