Detection of hubs in complex networks by the Laplacian matrix

Younghee Hong; Iksoo Chang; Choongrak Kim

doi:10.1007/s42952-020-00087-0

Detection of hubs in complex networks by the Laplacian matrix

Younghee Hong
, Iksoo Chang
, Choongrak Kim

Department of Brain Sciences

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

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.

Original language	English
Pages (from-to)	431-446
Number of pages	16
Journal	Journal of the Korean Statistical Society
Volume	50
Issue number	2
DOIs	https://doi.org/10.1007/s42952-020-00087-0
State	Published - Jun 2021

Bibliographical note

Publisher Copyright:
© 2020, Korean Statistical Society.

Keywords

Adjacency matrix
Conditional dependency
Eigenvalue
Eigenvector

Access to Document

10.1007/s42952-020-00087-0

Cite this

@article{337a98e6f75d4b1bbde6ebf350492b58,

title = "Detection of hubs in complex networks by the Laplacian matrix",

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.",

keywords = "Adjacency matrix, Conditional dependency, Eigenvalue, Eigenvector",

author = "Younghee Hong and Iksoo Chang and Choongrak Kim",

note = "Publisher Copyright: {\textcopyright} 2020, Korean Statistical Society.",

year = "2021",

month = jun,

doi = "10.1007/s42952-020-00087-0",

language = "English",

volume = "50",

pages = "431--446",

journal = "Journal of the Korean Statistical Society",

issn = "1226-3192",

publisher = "Korean Statistical Society",

number = "2",

}

TY - JOUR

T1 - Detection of hubs in complex networks by the Laplacian matrix

AU - Hong, Younghee

AU - Chang, Iksoo

AU - Kim, Choongrak

PY - 2021/6

Y1 - 2021/6

N2 - 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.

AB - 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.

KW - Adjacency matrix

KW - Conditional dependency

KW - Eigenvalue

KW - Eigenvector

UR - https://www.scopus.com/pages/publications/85091727121

U2 - 10.1007/s42952-020-00087-0

DO - 10.1007/s42952-020-00087-0

M3 - Article

AN - SCOPUS:85091727121

SN - 1226-3192

VL - 50

SP - 431

EP - 446

JO - Journal of the Korean Statistical Society

JF - Journal of the Korean Statistical Society

IS - 2

ER -

Detection of hubs in complex networks by the Laplacian matrix

Abstract

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this