eigenvector_centrality_und.m
3209cc2b0b1dmaster

eigenvector_centrality_und.m
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			function v = eigenvector_centrality_und(CIJ)
			%EIGENVECTOR_CENTRALITY_UND Spectral measure of centrality
			%
			% v = eigenvector_centrality_und(CIJ)
			%
			% Eigenector centrality is a self-referential measure of centrality:
			% nodes have high eigenvector centrality if they connect to other nodes
			% that have high eigenvector centrality. The eigenvector centrality of
			% node i is equivalent to the ith element in the eigenvector
			% corresponding to the largest eigenvalue of the adjacency matrix.
			%
			% Inputs: CIJ, binary/weighted undirected adjacency matrix.
			%
			% Outputs: v, eigenvector associated with the largest
			% eigenvalue of the adjacency matrix CIJ.
			%
			% Reference: Newman, MEJ (2002). The mathematics of networks.
			%
			% Contributors:
			% Xi-Nian Zuo, Chinese Academy of Sciences, 2010
			% Rick Betzel, Indiana University, 2012
			% Mika Rubinov, University of Cambridge, 2015

			% MODIFICATION HISTORY
			% 2010/2012: original (XNZ, RB)
			% 2015: ensure the use of leading eigenvector (MR)


			n = length(CIJ);
			if n < 1000
			[V,D] = eig(CIJ);
			else
			[V,D] = eigs(sparse(CIJ));
			end
			[~,idx] = max(diag(D));
			ec = abs(V(:,idx));
			v = reshape(ec, length(ec), 1);