Cooperator and Defector-Based Dynamic Community Detection in Social Networks

Dynamic community detection in social networks requires advanced methods to capture the intricate patterns of user interactions. Traditional approaches often fail to account for the evolving nature of social relationships, leading to incomplete community representations. This work addresses this gap by distinguishing between two key interaction types: cooperators, who foster collaboration, and defectors, who disrupt collective action. This behavioral classification provides deeper insights into community formation and stability. By integrating interaction-based data, the model enhances the accuracy of dynamic community detection, revealing hidden patterns and relationships beyond structural analysis. The model was rigorously evaluated on two real-world datasets, demonstrating superior performance over state-of-the-art baseline methods across multiple metrics. Comparative analysis confirms its enhanced ability to detect meaningful community structures within dynamic networks. This interaction-focused framework offers significant improvements in understanding social dynamics, capturing evolving relationships, and accurately modeling human cooperation within social networks.