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Clustering complex networks and biological networks by nonnegative matrix factorization with various similarity measures
- Source :
-
Neurocomputing . Dec2008, Vol. 72 Issue 1-3, p134-141. 8p. - Publication Year :
- 2008
-
Abstract
- Abstract: Identifying community structure in complex networks is closely related to clustering of data in other areas without an underlying network structure. In this paper, we propose a nonnegative matrix factorization (NMF)-based method for finding community structure. We first evaluate several similarity measures, such as diffusion kernel similarity, shortest path based similarity on several widely well-studied networks. Then, we apply NMF with diffusion kernel similarity to a large biological network, which demonstrates that our method can find biologically meaningful functional modules. Comparison with other algorithms also indicates the good performance of our method. [Copyright &y& Elsevier]
Details
- Language :
- English
- ISSN :
- 09252312
- Volume :
- 72
- Issue :
- 1-3
- Database :
- Academic Search Index
- Journal :
- Neurocomputing
- Publication Type :
- Academic Journal
- Accession number :
- 35326904
- Full Text :
- https://doi.org/10.1016/j.neucom.2007.12.043