Community detection in very large dense network with parallel strategy

Discovering the latent communities is a useful way to better understand the properties of a network. However, the typical size of virtual spaces is now counted in millions, if not billions, of nodes and edges, most existing algorithms are incapable to analyze such large scale dense networks. In this paper, a fast parallel modularity optimization algorithm that performs the analogous greedy optimization as CNM and FUC is used to conduct community discovering. By using the parallel manner and sophisticated data structures, its running time is essentially fast. In the experimental work, we evaluate our method using real datasets and compare our approach with several previous methods; the results show that our method is more effective in find potential online communities.