Improving community detection in social networks using enhanced BSO by exploring network structure

Abstract

Community detection in social networks plays a crucial role in understanding various social phenomena. However, accurately identifying communities is challenging due to the complex nature of social networks. To address this challenge, we propose CD-BSO, an innovative metaheuristic based on brainstorming. CD-BSO leverages network knowledge through specialized initialization and solution generation operators designed to capture social network characteristics. In the initialization phase, a technique combining Depth-First Search (DFS) is employed to consider both connectivity and information diffusion, ensuring accurate community representation. The generation step introduces search operators that consider link formation and node similarity, facilitating convergence towards a community structure that closely resembles the network's actual structure. The evaluation of CD-BSO outperforms recent and well-known community detection methods. The evaluation results were further analyzed using visual analysis, providing valuable insights into the network structure. CD-BSO exhibits significant potential for accurately identifying communities and extracting meaningful information from social networks.