Overlapping Community Structure Detection of Brain Functional Network Using Non-negative Matrix Factorization

• Published in: Neural Information Processing pp. 140 - 147
• Main Authors: Li, Xuan, Hu, Zilan, Wang, Haixian
• Format: Book Chapter
• Language: English
• Published: Cham Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Brain functional network; fMRI; Non-negative matrix factorization (nmf); Overlapping community
• ISBN: 3319466747; 9783319466743
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-46675-0_16

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.