A KLT-inspired node centrality for identifying influential neighborhoods in graphs

Muhammad U. Ilyas; Hayder Radha

doi:10.1109/CISS.2010.5464971

A KLT-inspired node centrality for identifying influential neighborhoods in graphs

Muhammad U. Ilyas, Hayder Radha

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

We present principal component centrality (PCC) as a measure of centrality that is more general and encompasses eigenvector centrality (EVC). We explain some of the difficulties in applying EVC to graphs and networks that contain more than just one neighborhood of nodes with high influence. We demonstrate the shortcomings of traditional EVC and contrast it against PCC. PCC's ranking procedure is based on spectral analysis of the network's graph adjacency matrix and identification of its most significant eigenvectors.

Original language	English
Title of host publication	2010 44th Annual Conference on Information Sciences and Systems (CISS)
Publisher	IEEE
Pages	1-7
Number of pages	7
ISBN (Print)	978-1-4244-7416-5
DOIs	https://doi.org/10.1109/CISS.2010.5464971
Publication status	Published - 19 Mar 2010
Event	2010 44th Annual Conference on Information Sciences and Systems (CISS) - Princeton, NJ, USA Duration: 17 Mar 2010 → 19 Mar 2010

Conference

Conference	2010 44th Annual Conference on Information Sciences and Systems (CISS)
Period	17/03/10 → 19/03/10

Keywords

Spectral analysis

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10.1109/CISS.2010.5464971

https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5464971

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title = "A KLT-inspired node centrality for identifying influential neighborhoods in graphs",

abstract = "We present principal component centrality (PCC) as a measure of centrality that is more general and encompasses eigenvector centrality (EVC). We explain some of the difficulties in applying EVC to graphs and networks that contain more than just one neighborhood of nodes with high influence. We demonstrate the shortcomings of traditional EVC and contrast it against PCC. PCC's ranking procedure is based on spectral analysis of the network's graph adjacency matrix and identification of its most significant eigenvectors.",

keywords = "Spectral analysis",

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T1 - A KLT-inspired node centrality for identifying influential neighborhoods in graphs

AU - Ilyas, Muhammad U.

AU - Radha, Hayder

PY - 2010/3/19

Y1 - 2010/3/19

N2 - We present principal component centrality (PCC) as a measure of centrality that is more general and encompasses eigenvector centrality (EVC). We explain some of the difficulties in applying EVC to graphs and networks that contain more than just one neighborhood of nodes with high influence. We demonstrate the shortcomings of traditional EVC and contrast it against PCC. PCC's ranking procedure is based on spectral analysis of the network's graph adjacency matrix and identification of its most significant eigenvectors.

AB - We present principal component centrality (PCC) as a measure of centrality that is more general and encompasses eigenvector centrality (EVC). We explain some of the difficulties in applying EVC to graphs and networks that contain more than just one neighborhood of nodes with high influence. We demonstrate the shortcomings of traditional EVC and contrast it against PCC. PCC's ranking procedure is based on spectral analysis of the network's graph adjacency matrix and identification of its most significant eigenvectors.

KW - Spectral analysis

UR - https://ieeexplore.ieee.org/document/5464971/

U2 - 10.1109/CISS.2010.5464971

DO - 10.1109/CISS.2010.5464971

M3 - Conference contribution

SN - 978-1-4244-7416-5

SP - 1

EP - 7

BT - 2010 44th Annual Conference on Information Sciences and Systems (CISS)

PB - IEEE

T2 - 2010 44th Annual Conference on Information Sciences and Systems (CISS)

Y2 - 17 March 2010 through 19 March 2010

ER -

A KLT-inspired node centrality for identifying influential neighborhoods in graphs

Abstract

Conference

Keywords

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