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demo_dis_rand.m
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demo_dis_rand.m
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clear;
%Example demonstration of the ranodm initialization for disjoint communoty detection
%====================
%Load the network datasets
%file = load('data\washington.mat');
%edges = file.washington.adj;
%adj = Compute_adjmatrix(edges); %Adjacent matrix
%att = file.washington.content; %Network attribute matrix
%gnd = file.washington.groundtruth(:,2); %Ground-truth of community membership
%==========
file = load('data\texas.mat');
edges = file.texas.adj;
adj = Compute_adjmatrix(edges); %Adjacent matrix
att = file.texas.content; %Adjacent matrix
gnd = file.texas.groundtruth(:,2); %Adjacent matrix
%==========
%file = load('data\cora.mat');
%adj = file.G; %Adjacent matrix
%att = file.content; %Network attribute matrix
%gnd = file.labels; %Ground-truth of community membership
%==========
%file = load('data\cite.mat');
%adj = file.G; %Adjacent matrix
%att = file.content; %Network attribute matrix
%gnd = file.labels; %Ground-truth of community membership
%==========
%Get the network parameters
num_nodes = size(adj, 1); %Number of nodes
num_atts = size(att, 2); %Number of node attributes
num_topo_clus = max(gnd); %Number of topology clusters
num_att_clus = num_topo_clus; %Number of attribtue clusters
%====================
max_iter = 1e4; %Maximum number of optimization iteratons
min_error = 1e-5; %Minimum relative error to determine the convergence of optimization
%==========
num_runs = 10; %Number of independent runs of DHCD
num_ch_runs = 10; %Number of independent runs of channel selection
sample_rate = 0.10; %Sampling rate of Ch-NMI
%==========
lambd = 0;
%====================
%Remove self-connectd edges
for i=1:num_nodes
adj(i, i) = 0;
end
%==========
adj = sparse(adj);
att = sparse(att);
%p = parpool;
%====================
params = [0.1:0.1:0.9, 1:1:10];
[~, num_params] = size(params); %Number of parameter settings
for l=1:num_params
%==============================
%T-A Channel, i.e., DHCD T-A
alpha = params(l);
fprintf('Alpha: %f\n', alpha);
%====================
[topo_mem_init, ~] = NMF_rand_init(adj, num_topo_clus); %Initialize the topology cluster membership matrix, i.e., X
[~, att_desc_init] = NMF_rand_init(att, num_att_clus); %Initialize the attribute description matrix, i.e., Z
trans_TA_init = rand(num_topo_clus, num_att_clus); %Initialize the T-A transition matrix, i.e., U
[topo_mem_TA,att_desc_TA,trans_TA,obj] = DHCD_TA(adj,att,topo_mem_init,att_desc_init,trans_TA_init,alpha,lambd,max_iter,min_error);
fprintf('T-A Obj. %8.4f\n', obj);
%==========
[~, labels] = max(topo_mem_TA, [], 2); %Extract the community detection result from X
chNMI = get_Ch_NMI(labels, gnd, sample_rate, num_ch_runs); %Derive the corresponding Ch-NMI
fprintf('Ch-NMI %f\n', chNMI);
%==========
%Evaluate the performance of current community detection result
NMI_TA = compute_NMI(gnd, labels);
res = bestMap(gnd, labels);
AC_TA = length(find(gnd == res))/length(gnd);
fprintf('T-A Ch. NMI: %8.4f; AC: %8.4f\n', NMI_TA, AC_TA);
fid = fopen('DHCD_T-A(dis_rand).txt', 'at');
fprintf(fid, 'T-A Ch. Alpha: %8.4f; Obj: %8.4f; Ch-NMI: %8.4f; NMI: %8.4f; AC: %8.4f\n', [alpha, obj, chNMI, NMI_TA, AC_TA]);
fclose(fid);
%====================
%Independently run the DHCD T-A algorihtm multiple times
for t=2:num_runs
[topo_mem_init, ~] = NMF_rand_init(adj, num_topo_clus); %Initialize the topology cluster membership matrix, i.e., X
[~, att_desc_init] = NMF_rand_init(att, num_att_clus); %Initialize the attribute cluster membership matrix, i.e., Z
trans_TA_init = rand(num_topo_clus, num_att_clus); %Initialize the T-A transition matrix, i.e., U
[cur_topo_mem_TA,cur_att_desc_TA,cur_trans_TA,cur_obj] = DHCD_TA(adj,att,topo_mem_init,att_desc_init,trans_TA_init,alpha,lambd,max_iter,min_error);
fprintf('T-A Obj. %8.4f\n', cur_obj);
%==========
[~, labels] = max(cur_topo_mem_TA, [], 2); %Extract the community detection result from X
chNMI = get_Ch_NMI(labels, gnd, sample_rate, num_ch_runs); %Derive the corresponding Ch-NMI
%==========
%Evaluate the performance of current community detection result
fprintf('Ch-NMI %f\n', chNMI);
NMI_TA = compute_NMI(gnd, labels);
res = bestMap(gnd, labels);
AC_TA = length(find(gnd == res))/length(gnd);
fprintf('T-A Ch. NMI: %8.4f; AC: %8.4f\n', NMI_TA, AC_TA);
%==========
fid = fopen('DHCD_T-A(dis_rand).txt', 'at');
fprintf(fid, 'T-A Ch. Alpha: %8.4f; Obj: %8.4f; Ch-NMI: %8.4f; NMI: %8.4f; AC: %8.4f\n', [alpha, cur_obj, chNMI, NMI_TA, AC_TA]);
fclose(fid);
%====================
%Update the best community detection result based on the value of objective function
if cur_obj<obj
topo_mem_TA = cur_topo_mem_TA;
att_desc_TA = cur_att_desc_TA;
trans_TA = cur_trans_TA;
obj = cur_obj;
end
end
%====================
%Evalute the performance of the best community detection result
[~, labels] = max(topo_mem_TA, [], 2);
NMI_TA = compute_NMI(gnd, labels);
res = bestMap(gnd, labels);
AC_TA = length(find(gnd == res))/length(gnd);
fprintf('(Obj. Min.)T-A Ch. NMI: %8.4f; AC: %8.4f\n', NMI_TA, AC_TA);
fprintf('====================\n');
%===========
fid = fopen('DHCD_T-A(dis_rand).txt', 'at');
fprintf(fid, 'Obj. Min. T-A Ch. Alpha: %8.4f; Obj: %8.4f; NMI: %8.4f; AC: %8.4f\n', [alpha, obj, NMI_TA, AC_TA]);
fprintf(fid, '====================\n');
fclose(fid);
%==============================
%A-T Channel, i.e., DHCD A-T
beta = alpha;
fprintf('Beta: %f\n', beta);
%====================
%Initialize the attribute cluster membership matrix & attribute description matrix, i.e., Y & Z
[att_mem_init, att_desc_init] = NMF_rand_init(att, num_att_clus);
trans_AT_init = rand(num_att_clus, num_topo_clus); %Initialize the A-T transition matrix, i.e., V
[att_desc_AT,att_mem_AT,trans_AT,obj] = DHCD_AT(adj,att,att_mem_init,att_desc_init,trans_AT_init,beta,lambd,max_iter,min_error);
fprintf('A-T Obj. %8.4f\n', obj);
%==========
[~, labels] = max(att_mem_AT, [], 2); %Extract the community detection result from Y
chNMI = get_Ch_NMI(labels, gnd, sample_rate, num_ch_runs); %Derive the corresponding Ch-NMI
fprintf('Ch-NMI %f\n', chNMI);
%==========
%Evaluate the performance of current community detection result
NMI_AT = compute_NMI(gnd, labels);
res = bestMap(gnd, labels);
AC_AT = length(find(gnd == res))/length(gnd);
fprintf('A-T Ch. NMI: %8.4f; AC: %8.4f\n', NMI_AT, AC_AT);
fid = fopen('DHCD_A-T(dis_rand).txt', 'at');
fprintf(fid, 'A-T Ch. Beta: %8.4f; Obj: %8.4f; Ch-NMI: %8.4f; NMI: %8.4f; AC: %8.4f\n', [beta, obj, chNMI, NMI_AT, AC_AT]);
fclose(fid);
%====================
%Independently run the DHCD A-T algorihtm multiple times
for t=2:num_runs
%Initialize the attribute cluster membership matrix & attribute description matrix, i.e., Y & Z
[att_mem_init, att_desc_init] = NMF_rand_init(att, num_att_clus);
trans_AT_init = rand(num_att_clus, num_topo_clus); %Initialize the A-T transition matrix, i.e., V
[cur_att_desc_AT,cur_att_mem_AT,cur_trans_AT,cur_obj] = DHCD_AT(adj,att,att_mem_init,att_desc_init,trans_AT_init,beta,lambd,max_iter,min_error);
fprintf('A-T Obj. %8.4f\n', cur_obj);
%==========
[~, labels] = max(cur_att_mem_AT, [], 2); %Extract the community detection result from Y
chNMI = get_Ch_NMI(labels, gnd, sample_rate, num_ch_runs); %Derive the corresponding Ch-NMI
fprintf('Ch-NMI %f\n', chNMI);
%==========
%Evaluate the performance of current community detection result
NMI_AT = compute_NMI(gnd, labels);
res = bestMap(gnd, labels);
AC_AT = length(find(gnd == res))/length(gnd);
fprintf('A-T Ch. NMI: %8.4f; AC: %8.4f\n', NMI_AT, AC_AT);
fid = fopen('DHCD_A-T(dis_rand).txt', 'at');
fprintf(fid, 'A-T Ch. Beta: %8.4f; Obj: %8.4f; Ch-NMI: %8.4f; NMI: %8.4f; AC: %8.4f\n', [beta, cur_obj, chNMI, NMI_AT, AC_AT]);
fclose(fid);
%===================
%Update the best community detection result based on the value of objective function
if cur_obj<obj
att_mem_AT = cur_att_mem_AT;
att_desc_AT = cur_att_desc_AT;
trans_AT = cur_trans_AT;
obj = cur_obj;
end
end
%======================
%Evalute the performance of the best community detection result
[~, labels] = max(att_mem_AT, [], 2);
NMI_AT = compute_NMI(gnd, labels);
res = bestMap(gnd, labels);
AC_AT = length(find(gnd == res))/length(gnd);
fprintf('(Obj. Min.)A-T Ch. NMI: %8.4f; AC: %8.4f\n', NMI_AT, AC_AT);
fprintf('====================\n');
%==========
fid = fopen('DHCD_A-T(dis_rand).txt', 'at');
fprintf(fid, 'Obj. Min. A-T Ch. Beta: %8.4f; Obj: %8.4f; NMI: %8.4f; AC: %8.4f\n', [beta, obj, NMI_AT, AC_AT]);
fprintf(fid, '=====================\n');
fclose(fid);
end
%delete(p);