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Overlapping Community Detection Using Non-Negative Matrix Factorization With Orthogonal and Sparseness Constraints
- Source :
- IEEE Access, Vol 6, Pp 21266-21274 (2018)
- Publication Year :
- 2018
- Publisher :
- IEEE, 2018.
-
Abstract
- Network is an abstract expression of subjects and the relationships among them in the real-world system. Research on community detection can help people understand complex systems and identify network functionality. In this paper, we present a novel approach to community detection that utilizes a nonnegative matrix factorization (NMF) model to divide overlapping community from networks. The study is based on the different physical meanings of the pair of matrices $W$ and $H$ to optimize the constraint condition. Many community detection algorithms based on NMF require the number of known communities as a prior condition, which limits the field of application of the algorithms. This paper handled the problem by feature matrix preprocessing and ranking optimization, so that the proposed algorithm can divide the network structure with unknown community number. Experiments demonstrated that the proposed algorithm can effectively divide the community structure, and identify network overlay communities and overlapping nodes.
- Subjects :
- Theoretical computer science
General Computer Science
Computer science
Overlay network
02 engineering and technology
01 natural sciences
Matrix decomposition
Non-negative matrix factorization
non-negative matrix factorization
Matrix (mathematics)
sparse constraint
0103 physical sciences
0202 electrical engineering, electronic engineering, information engineering
Symmetric matrix
General Materials Science
010306 general physics
Cluster analysis
Sparse matrix
Community detection
orthogonal constraint
General Engineering
Community structure
Approximation algorithm
020201 artificial intelligence & image processing
lcsh:Electrical engineering. Electronics. Nuclear engineering
ranking optimization
lcsh:TK1-9971
Subjects
Details
- Language :
- English
- ISSN :
- 21693536
- Volume :
- 6
- Database :
- OpenAIRE
- Journal :
- IEEE Access
- Accession number :
- edsair.doi.dedup.....d4c18f94b7a493ea0eedaa53bd4cee76