A LOCALLY AND GLOBALLY TUNED METAHEURISTIC OPTIMIZATION FOR OVERLAPPING COMMUNITY DETECTION

Many people use online social networks to share their opinions and information in this digital age. The number of engaged dynamic nature pose a major challenge for network analysis (SNA). Community detection is one the most critical fascinating issues analysis. Researchers frequently employ node features topological structures recognize important meaningful performance order locate non-overlapping communities. We introduce locally globally tuned multi-objective biogeography-based optimization (LGMBBO) technique research detecting overlapping communities based on connections similarity. Four real- world datasets were used experiment assess quality both partitions. As result, model generates set solutions that have best structure with properties. suggested will increase productivity enhance abilities identify significant pertinent