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Greedy Approach for Unfolding Communities from Massive Networks Using Multi-Threading

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Document pages: 6 pages

Abstract: A couple of the Internet and large-scale public networks (i.e. YouTube, Facebook, LinkedIn, and Twitter etc.) are having a deep and prolific effect on the learning of social networks. Real-world social networks are to divide into small partitions. The aim of this research work is to develop sturdy and faster Community Detection Algorithms using a parallel approach with multi-threading, which finds strong groups from given social network. We have proposed a Greedy Incremental Community Detection using Multi-Threading (GICDMT) and it is tested on various well-known standard datasets with Eigenvector based community detection algorithm.

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