Abstract
Identifying strongly associated clusters in large complex networks has received an increased amount of interest since the past decade. The problem of community detection in complex networks is an NP complete problem that necessitates the clustering of a network into communities of compactly linked nodes in such a manner that the interconnection between the nodes is found to be denser than the intra-connection between the communities. In this paper, different approaches given by the authors in the field of community detection have been described with each methodology being classified according to algorithm type, along with the comparative analysis of these approaches on the basis of NMI and Modularity for four real world networks.
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