• Sayali Dhokare
• Gayatri Zalwar
• Komal Kumari
• Mrunal Bhavsar
• Kavita Sawant,

This system presents a novel hybrid method based on both optimization and hybrid hierarchical clustering for community structure in social networks. Because the hybrid hierarchical model usually generates the problem of convergence to a locally optimal detected community, we proposed a new modularity based opinion leader function and we introduced a met heuristic namely genetic algorithm to optimize this introduced quality unction. We introduce a genetic hybrid formulation of hierarchical clustering for community detection in social networks, where hierarchical bottom-up and top-down methods are combined to produce the same community structure. Performances of the proposed approach are evaluated using both real and artificial networks. Experiments show the efficiency of the introduced method in improving the execution time and enhancing the quality of the clustering results.

"CLASSIFICATION FOR COMMUNITY STRUCTURE IN SOCIAL MEDIA NETWORKS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 6, page no.848-849, June-2020, Available :http://www.jetir.org/papers/JETIR2006116.pdf

"CLASSIFICATION FOR COMMUNITY STRUCTURE IN SOCIAL MEDIA NETWORKS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 6, page no. pp848-849, June-2020, Available at : http://www.jetir.org/papers/JETIR2006116.pdf