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Combining Non-negative Matrix Factorization and Sparse Coding for Functional Brain Overlapping Community Detection

Authors
  • Li, X.1, 2
  • Hu, Z.3
  • Wang, H.1
  • 1 Foshan University, School of Mathematics and Big Data, Foshan, Guangdong, 528000, People’s Republic of China , Foshan (China)
  • 2 Southeast University, Research Center for Learning Science, Nanjing, Jiangsu, 210096, People’s Republic of China , Nanjing (China)
  • 3 Anhui University of Technology, School of Mathematics and Physics, Maanshan, Anhui, 243002, People’s Republic of China , Maanshan (China)
Type
Published Article
Journal
Cognitive Computation
Publisher
Springer-Verlag
Publication Date
Aug 01, 2018
Volume
10
Issue
6
Pages
991–1005
Identifiers
DOI: 10.1007/s12559-018-9585-6
Source
Springer Nature
Keywords
License
Yellow

Abstract

The functional system of the human brain can be viewed as a complex network. Among various features of the brain functional network, community structure has raised significant interest in recent years. Increasing evidence has revealed that most realistic complex networks have an overlapping community structure. However, the overlapping community structure of the brain functional network has not been adequately studied. In this paper, we propose a novel method called sparse symmetric non-negative matrix factorization (ssNMF) to detect the overlapping community structure of the brain functional network. Specifically, it is formulated by combining the effective techniques of non-negative matrix factorization and sparse coding. Besides, the non-negative adaptive sparse representation is applied to construct the whole-brain functional network, based on which ssNMF is performed to detect the community structure. Both simulated and real functional magnetic resonance imaging data are used to evaluate ssNMF. The experimental results demonstrate that the proposed ssNMF method is capable of accurately and stably detecting the underlying overlapping community structure. Moreover, the physiological interpretation of the overlapping community structure detected by ssNMF is straightforward. This novel framework, we think, provides an effective tool to study overlapping community structure and facilitates the understanding of the network organization of the functional human brain.

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