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example.m
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example.m
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function example
restoredefaultpath
addpath(genpath('utils'))
addpath(genpath('subroutines'))
addpath(genpath('SpectralClusteringOfSignedGraphsViaMatrixPowerMeans'))
%% Example where the first layer is informative and the second is not informative
numLayers = 2;
numNodes = 100;
numClusters = 2;
groundTruth = zeros(numNodes,1);
groundTruth(1:numNodes/2) = 1;
GroundTruthPerLayerCell = {groundTruth, groundTruth};
pinVec = [0.9 0.8];
poutVec = [0.1 0.2];
s = RandStream('mcg16807','Seed',0); RandStream.setGlobalStream(s);
Wcell = generate_multilayer_graph(numLayers, GroundTruthPerLayerCell, pinVec, poutVec);
% visualize adjacency matrices
figure, hold on
subplot(1,2,1), spy(Wcell{1}), title('Positive Edges')
subplot(1,2,2), spy(Wcell{2}), title('Negative Edges')
figure, hold on
% Clustering with p = 10
p = 10;
s = RandStream('mcg16807','Seed',0); RandStream.setGlobalStream(s);
C = clustering_signed_graphs_with_power_mean_laplacian(Wcell, p, numClusters);
subplot(1,5,1), stem(C), title('p=10')
1;
% Clustering with p = 1 (Arithmetic Mean)
p = 1;
s = RandStream('mcg16807','Seed',0); RandStream.setGlobalStream(s);
C = clustering_signed_graphs_with_power_mean_laplacian(Wcell, p, numClusters);
subplot(1,5,2), stem(C), title('p=1')
1;
1;
% Clustering with p -> 0 (log euclidean Mean)
p = 1;
s = RandStream('mcg16807','Seed',0); RandStream.setGlobalStream(s);
C = clustering_signed_graphs_with_power_mean_laplacian(Wcell, p, numClusters);
subplot(1,5,3), stem(C), title('p->0')
1;
1;
% Clustering with p = -1 (Harmonic Mean)
p = -1;
s = RandStream('mcg16807','Seed',0); RandStream.setGlobalStream(s);
C = clustering_signed_graphs_with_power_mean_laplacian(Wcell, p, numClusters);
subplot(1,5,4), stem(C), title('p=-1')
1;
1;
% Clustering with p = -10
p = -10;
s = RandStream('mcg16807','Seed',0); RandStream.setGlobalStream(s);
C = clustering_signed_graphs_with_power_mean_laplacian(Wcell, p, numClusters);
subplot(1,5,5), stem(C), title('p=-10')
1;
1;