Department of Brain Sciences
Theoretical and Computational Biophysics Laboratory
1. Journal Articles
Detection of hubs in complex networks by the Laplacian matrix
- Title
- Detection of hubs in complex networks by the Laplacian matrix
- Author(s)
- Hong, Younghee ; Chang, Iksoo ; Kim, Choongrak
- Issued Date
- 2021-06
- Citation
- Journal of the Korean Statistical Society, v.50, no.2, pp.431 - 446
- 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.
- Publisher
- Springer
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- Appears in Collections:
- Department of Brain Sciences Theoretical and Computational Biophysics Laboratory 1. Journal Articles
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