DocumentCode
2367497
Title
A KLT-inspired node centrality for identifying influential neighborhoods in graphs
Author
Ilyas, Muhammad U. ; Radha, Hayder
Author_Institution
Dept. of Electr. & Comput. Eng., Michigan State Univ., East Lansing, MI, USA
fYear
2010
fDate
17-19 March 2010
Firstpage
1
Lastpage
7
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
Karhunen-Loeve transforms; eigenvalues and eigenfunctions; graph theory; network theory (graphs); principal component analysis; KLT inspired node centrality; eigenvector centrality; graphs; influential neighborhood identification; most significant eigenvector; network graph adjacency matrix; principal component centrality; spectral analysis; Spectral analysis;
fLanguage
English
Publisher
ieee
Conference_Titel
Information Sciences and Systems (CISS), 2010 44th Annual Conference on
Conference_Location
Princeton, NJ
Print_ISBN
978-1-4244-7416-5
Electronic_ISBN
978-1-4244-7417-2
Type
conf
DOI
10.1109/CISS.2010.5464971
Filename
5464971
Link To Document