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iCAPs_v2/packages/gspbox/utils/gsp_sort_nodes.m
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gsp_sort_nodes.m
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function [W_sorted, ind_sort, modularity, com_id, com_sizes] = gsp_sort_nodes(W, recursive, self_loops)
%GSP_SORT_NODES Sort nodes using the Louvain clustering method
% Usage: [Wout, ind, modularity, com_id, com_sizes] = gsp_sort_nodes(W)
% [Wout, ind, modularity, com_id, com_sizes] = gsp_sort_nodes(W, recursive)
% [Wout, ind, modularity, com_id, com_sizes] = gsp_sort_nodes(W, recursive, self_loops)
%
% Inputs:
% W : Weigthed adjacency matrix. Can contain self-loops
% recursive : Use recursive computation (default: 1)
% self_loops: Take into account self loops (default: 0)
%
% Outputs:
% Wout : Sorted weighted adjacency matrix
% ind : Indices used for sorting: Wout = W(ind, ind)
% modularity: Modularity of final clustering used for sorting
% com_id : Community ID of each node in the initial W
% com_sizes : Number of nodes in each community
%
% gsp_sort_nodes(W) sorts the nodes of the weighted adjacency matrix W
% by clustering them according to the "Louvain" method.
%
% By default the clustering is recursive and the level of recursion that
% gives the maximum modularity is kept. The nodes of the graph are sorted
% according to the clusters given.
%
% The clustering method is using code given free online by Antoine
% Scherrer available here:
% https://perso.uclouvain.be/vincent.blondel/research/louvain.html
%
% Example:
%
% G = gsp_nn_graph([[randn(100, 1)-1; (randn(100, 1)+7)], ...
% [randn(50, 1); (randn(100,1)-4); (randn(50, 1)*2+8)]]);
% [Wout, ind, modularity, com_id, com_sizes] = gsp_sort_nodes(G.W);
% figure; imagesc(Wout); title('sorted adjacency matrix');
% figure; gsp_plot_signal(G, ind);
%
%
%
% See also: cluster_jl
%
% References:
% V. D. Blondel, J.-L. Guillaume, R. Lambiotte, and E. Lefebvre. Fast
% unfolding of communities in large networks. Journal of statistical
% mechanics: theory and experiment, 2008(10):P10008, 2008.
%
%
%
% Url: http://lts2research.epfl.ch/gsp/doc/utils/gsp_sort_nodes.php
% Copyright (C) 2013-2016 Nathanael Perraudin, Johan Paratte, David I Shuman.
% This file is part of GSPbox version 0.7.0
%
% 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 3 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, see <http://www.gnu.org/licenses/>.
% If you use this toolbox please kindly cite
% N. Perraudin, J. Paratte, D. Shuman, V. Kalofolias, P. Vandergheynst,
% and D. K. Hammond. GSPBOX: A toolbox for signal processing on graphs.
% ArXiv e-prints, Aug. 2014.
% http://arxiv.org/abs/1408.5781
% Author: Vassilis Kalofolias
% Date: June 2016
if nargin < 2
% Use recursive clustering and pick the level with the highest modularity
recursive = 1;
end
if nargin < 3
% Don't use self loops by default
self_loops = 0;
end
debug = 0;
verbose = 0;
COMTY = cluster_jl(W, recursive, self_loops, debug, verbose);
% Keep as best the splitting with highest modularity
ind_best_splitting = argmax(COMTY.MOD);
% Which node belongs to which community
com_id = COMTY.COM{ind_best_splitting};
% Sort according to the communities
[~, ind_sort] = sort(com_id);
modularity = COMTY.MOD(ind_best_splitting);
com_sizes = COMTY.SIZE{ind_best_splitting};
W_sorted = W(ind_sort, ind_sort);
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