📄 laplacian.m
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function L=laplacian(edges,weights,N)%Function L=laplacian(edges,weights,N) computes the weighted % Laplacian matrix of a point and edge set. For an unweighted % matrix, set weights equal to ones(1,M) where M is the number % of edges in the graph. %%Inputs: edges - A Mx2 list of M edges indexing into points% weights - The weights used to determine matrix values. % If not specified, uses vector of all ones% N - Optional number of nodes in the graph. Used if% N > max(edges) (i.e., isolated nodes are present)%%Outputs: L - Laplacian matrix%%%6/5/03 - Leo Grady% Copyright (C) 2002, 2003 Leo Grady <lgrady@cns.bu.edu>% Computer Vision and Computational Neuroscience Lab% Department of Cognitive and Neural Systems% Boston University% Boston, MA 02215%% This program is free software; you can redistribute it and/or% modify it under the terms of the GNU General Public License% as published by the Free Software Foundation; either version 2% of the License, or (at your option) any later version.%% This program is distributed in the hope that it will be useful,% but WITHOUT ANY WARRANTY; without even the implied warranty of% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the% GNU General Public License for more details.%% You should have received a copy of the GNU General Public License% along with this program; if not, write to the Free Software% Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA.%% Date - $Id: laplacian.m,v 1.2 2003/08/21 17:29:29 lgrady Exp $%========================================================================%%If weights are not specified, use unity weightingif nargin == 1 weights=ones(size(edges,1),1);end%If N is not specified, use maximum values of edgesif nargin < 3 N=max(max(edges));end%Build sparse Laplacian matrixW=adjacency(edges,weights,N);L=diag(sum(W))-W;
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