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Detection of hubs in complex networks by the Laplacian matrix

Title
Detection of hubs in complex networks by the Laplacian matrix
Authors
Hong, YoungheeChang, IksooKim, Choongrak
DGIST Authors
Hong, Younghee; Chang, Iksoo; Kim, Choongrak
Issue Date
2021-06
Citation
Journal of the Korean Statistical Society, 50(2), 431-446
Type
Article
Article Type
Article in press
Author Keywords
Adjacency matrixConditional dependencyEigenvalueEigenvector
Keywords
DIMENSIONAL COVARIANCE ESTIMATIONLARGEST EIGENVALUEEXPRESSIONSELECTIONREGRESSIONENSEMBLESRATESMODEL
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.
URI
http://hdl.handle.net/20.500.11750/12435
DOI
10.1007/s42952-020-00087-0
Publisher
Springer
Related Researcher
  • Author Chang, Iksoo Theoretical and Computational Biophysics Laboratory
  • Research Interests Theoretical and Computational Biophysics; Supercomputing Simulation of Biomolecules; 이론?계산 생물물리학; 통계물리학; 단백질체의 슈퍼컴퓨터 모델링 및 시물레이션
Files:
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Collection:
Department of Brain and Cognitive SciencesTheoretical and Computational Biophysics Laboratory1. Journal Articles


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