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

  • Xuan Li
  • Zilan Hu
  • Haixian WangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9949)


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.


Overlapping community Non-negative matrix factorization (nmf) Brain functional network fMRI 



This work was supported in part by the National Basic Research Program of China under Grant 2015CB351704, the National Natural Science Foundation of China under Grant 61375118, and the Research Foundation for Young Teachers in Anhui University of Technology under Grant QZ201516.


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Key Lab of Child Development and Learning Science of Ministry of Education, Research Center for Learning ScienceSoutheast UniversityNanjingPeople’s Republic of China
  2. 2.School of Mathematics and PhysicsAnhui University of TechnologyMaanshanPeople’s Republic of China

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