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
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;
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
DOI :
10.1109/CISS.2010.5464971
