Source:http://linkedlifedata.com/resource/pubmed/id/17500946
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| Predicate | Object |
|---|---|
| rdf:type | |
| lifeskim:mentions | |
| pubmed:issue |
4 Pt 2
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| pubmed:dateCreated |
2007-5-15
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| pubmed:abstractText |
Based on cluster desynchronization properties of phase oscillators, we introduce an efficient method for the detection and identification of modules in complex networks. The performance of the algorithm is tested on computer generated and real-world networks whose modular structure is already known or has been studied by means of other methods. The algorithm attains a high level of precision, especially when the modular units are very mixed and hardly detectable by the other methods, with a computational effort O(KN) on a generic graph with N nodes and K links.
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| pubmed:language |
eng
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| pubmed:journal | |
| pubmed:status |
PubMed-not-MEDLINE
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| pubmed:month |
Apr
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| pubmed:issn |
1539-3755
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| pubmed:author | |
| pubmed:issnType |
Print
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| pubmed:volume |
75
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| pubmed:owner |
NLM
|
| pubmed:authorsComplete |
Y
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| pubmed:pagination |
045102
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| pubmed:year |
2007
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| pubmed:articleTitle |
Detecting complex network modularity by dynamical clustering.
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| pubmed:affiliation |
CNR-Istituto dei Sistemi Complessi, Via Madonna del Piano, 10, 50019 Sesto Fiorentino, FI, Italy.
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| pubmed:publicationType |
Journal Article
|