A Review on Various Community Detection Methods in Massive Networks Using Graph Mining

Nowadays, massive social networks like Facebook, Twitter, LinkedIn, Google+ has gained remarkable attention of Peoples and these massive networks have a tremendous amount of multifarious data which analysis can lead to identifying unknown information and relations among such networks. Social network analysis (SNA) is a difficult task in a recent situation. The peoples are more attached to these social networks sites cause them to produced massive data. So this massive network's data are very much complex to analyze manually. Community detection in sizably voluminous-scale gregarious networks becomes more consequential. A community is a subset of nodes in networks so that nodes in the same community are more densely connected than nodes in a various community. An unfolding of communities is important to understand the structure of massive networks. Data mining provides many techniques and algorithms to unfold communities among these massive networks. A community can be represented using graph so Community has different parameters like modularity, conductance, density etc. and by improving one of these parameters we can detect better community. So to detect community is a challenging task in the recent scenario. This paper represents a review of existing community detection algorithms and approaches in massive networks.