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Erschienen in:
Chaotic Feature Selection and Reconstruction in Time Series Prediction Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

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

verfasst von : Xuan Li, Zilan Hu, Haixian Wang

Erschienen in: Neural Information Processing

Verlag: Springer International Publishing

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Abstract

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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Vorheriges Kapitel Parcellating Whole Brain for Individuals by Simple Linear Iterative Clustering
Nächstes Kapitel Collaborative-Based Multi-scale Clustering in Very High Resolution Satellite Images
Fußnoten
1
Since the relationship between different communities is not the concern of this paper, we take the cluster of the last five nodes as an independent community, thus changing the number of communities in the overall ground truth from 8 to 9.
 
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Metadaten
Titel
Overlapping Community Structure Detection of Brain Functional Network Using Non-negative Matrix Factorization
verfasst von
Xuan Li
Zilan Hu
Haixian Wang
Copyright-Jahr
2016
Buch
Neural Information Processing

Print ISBN: 978-3-319-46674-3

Electronic ISBN: 978-3-319-46675-0

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-46675-0_16