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Detection of hubs in complex networks by the Laplacian matrix
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- Title
- Detection of hubs in complex networks by the Laplacian matrix
- Issued Date
- 2021-06
- Citation
- Hong, Younghee. (2021-06). Detection of hubs in complex networks by the Laplacian matrix. Journal of the Korean Statistical Society, 50(2), 431–446. doi: 10.1007/s42952-020-00087-0
- Type
- Article
- Author Keywords
- Adjacency matrix ; Conditional dependency ; Eigenvalue ; Eigenvector
- Keywords
- DIMENSIONAL COVARIANCE ESTIMATION ; LARGEST EIGENVALUE ; EXPRESSION ; SELECTION ; REGRESSION ; ENSEMBLES ; RATES ; MODEL
- ISSN
- 1226-3192
- Abstract
-
We propose a definition of hub in complex networks by using the eigenvectors of the Laplacian matrix, and suggest a method of detecting hubs. The proposed definition provides a different concept from the classical measures such as the centrality or degree. Also, a method of determining the number of hubs is suggested using a scree plot. Illustrative examples based on artificial data sets and real data sets are given. © 2020, Korean Statistical Society.
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- Publisher
- Springer
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