A LOCALLY AND GLOBALLY TUNED METAHEURISTIC OPTIMIZATION FOR OVERLAPPING COMMUNITY DETECTION

Abstract

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