Abstract
The human brain is one of the most complex systems that have been investigated and modeled. In order to explore its functions thoroughly, scientists have developed various ways of acquiring and analysing the brain signal. Recently, graph and network theory has gained great popularity in modeling the brain connectivity of different brain areas. In this paper, we propose a fast community detection algorithm based on a novel definition of connection density within the network. To validate the method, a set of artificially generated networks was used to benchmark the algorithm. A real fMRI data was also used to test the algorithm's capability of processing large-scale dataset.
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