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