Overlapping Community Structure Detection of Brain Functional Network Using Non negative Matrix Factorization
Community structure, as a main feature of a complex network, has been investigated recently under the assumption that the identified communities are non overlapping. However, few studies have revealed the overlapping community structure of the brain functional network, despite the fact that communities of most real networks overlap. In this paper, we propose a novel framework to identify the overlapping community structure of the brain functional network by using the symmetric non negative matrix factorization (SNMF) in which we develop a non negative adaptive sparse representation (NASR) to produce an association matrix. Experimental results on fMRI data sets show that, compared with modularity optimization, normalized cuts and affinity propagation, SNMF identifies the community structure more accurately and can shed new light on the understanding of brain functional systems.
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