Master

adj2gephilab/adj2gephilab.m

@@ -0,0 +1,67 @@
+function EdgeL=adj2gephilab(filename,ADJ,parameters)
+% Convert ana adjacency matrix of a graph to 2 spreadhseets csv files
+% one for the edge table and the other for node table.
+% The files _node.csv and _edge.csv have to be open 
+%in Gephi via Data Laboratory.
+% INPUTS:
+%           filename: string for the prefix name of the two files .csv
+%           ADJ: the adjacency matrix
+%           parameters: vector as properties of the node to use as
+%                              attributes of them.
+% OUTPUTS:
+%            two csv spreadsheet files: 
+%                       filename_node.csv
+%                       filename_edge.csv
+%             EdgeL = it returns the edge list corresponing to the
+%             adjacency matrix. (it can be saved to be open in Gephi too)
+%
+% The two files must be open in Gephi via the Data Laboratory
+
+nodecsv=[filename,'_node.csv'];
+edgecsv=[filename,'_edge.csv'];
+n=size(ADJ,1); % square adjacency matrix
+
+if nargin<3 
+    parameters=ones(n,1);% all nodes have the same attributes
+end
+ps=parameters;
+%% Node Table:
+% header for node csv
+fidN = fopen(nodecsv,'w','native','UTF-8'); % best format for gephi
+fprintf(fidN,'%s\n','Id;Label;Attribute');
+% 
+for i=2:n+1,
+    fprintf(fidN,'%s\n',[ num2str(i-1) ';"Node ' num2str(i-1) '"'...
+        ';' num2str(ps(i-1))]);
+end
+fclose(fidN);
+
+%% Edge Table
+EdgeL=conv_EdgeList(ADJ);
+
+S=EdgeL(:,1); % sources
+T=EdgeL(:,2); % targets
+W = EdgeL(:,3); % weights
+
+fidE = fopen(edgecsv,'w','native','UTF-8');
+% header for edge csv
+fprintf(fidE,'%s\n','Source;Target;Label;Weight');
+
+for i=2:length(S)+1,
+      fprintf(fidN,'%s\n',[ num2str(S(i-1)) ';' num2str(T(i-1)) ';'...
+          '"Edge from ' num2str(S(i-1)) ' to ' num2str(T(i-1)) '"' ';'...
+          num2str(W(i-1))]);
+end
+ fclose(fidE);
+
+%% Aux function
+function EdgeL=conv_EdgeList(adj)
+% convert adj matrix to edge list
+n=size(adj,1); % number of nodes
+edges=find(adj>0); % indices of all edges
+n_e=length(edges);
+EdgeL=zeros(n_e,3);
+for e=1:n_e
+  [i,j]=ind2sub([n,n],edges(e)); % node indices of edge e  
+  EdgeL(e,:)=[i j adj(i,j)];
+end

adj2gephilab/adj2gephilab2.m

@@ -0,0 +1,67 @@
+function EdgeL=adj2gephilab2(filename,ADJ,parameters,idx)
+% Convert ana adjacency matrix of a graph to 2 spreadhseets csv files
+% one for the edge table and the other for node table.
+% The files _node.csv and _edge.csv have to be open 
+%in Gephi via Data Laboratory.
+% INPUTS:
+%           filename: string for the prefix name of the two files .csv
+%           ADJ: the adjacency matrix
+%           parameters: vector as properties of the node to use as
+%                              attributes of them.
+% OUTPUTS:
+%            two csv spreadsheet files: 
+%                       filename_node.csv
+%                       filename_edge.csv
+%             EdgeL = it returns the edge list corresponing to the
+%             adjacency matrix. (it can be saved to be open in Gephi too)
+%
+% The two files must be open in Gephi via the Data Laboratory
+
+nodecsv=[filename,'_node.csv'];
+edgecsv=[filename,'_edge.csv'];
+n=size(ADJ,1); % square adjacency matrix
+
+if nargin<3 
+    parameters=ones(n,1);% all nodes have the same attributes
+end
+ps=parameters;
+%% Node Table:
+% header for node csv
+fidN = fopen(nodecsv,'w','native','UTF-8'); % best format for gephi
+fprintf(fidN,'%s\n','Id;Label;Attribute');
+% 
+for i=2:n+1,
+    fprintf(fidN,'%s\n',[ num2str(idx(i-1)) ';"Node ' num2str(idx(i-1)) '"'...
+        ';' num2str(ps(i-1))]);
+end
+fclose(fidN);
+
+%% Edge Table
+EdgeL=conv_EdgeList(ADJ,idx);
+
+S=EdgeL(:,1); % sources
+T=EdgeL(:,2); % targets
+W = EdgeL(:,3); % weights
+
+fidE = fopen(edgecsv,'w','native','UTF-8');
+% header for edge csv
+fprintf(fidE,'%s\n','Source;Target;Label;Weight');
+
+for i=2:length(S)+1,
+      fprintf(fidN,'%s\n',[ num2str(S(i-1)) ';' num2str(T(i-1)) ';'...
+          '"Edge from ' num2str(S(i-1)) ' to ' num2str(T(i-1)) '"' ';'...
+          num2str(W(i-1))]);
+end
+ fclose(fidE);
+
+%% Aux function
+function EdgeL=conv_EdgeList(adj,idx)
+% convert adj matrix to edge list
+n=size(adj,1); % number of nodes
+edges=find(adj>0); % indices of all edges
+n_e=length(edges);
+EdgeL=zeros(n_e,3);
+for e=1:n_e
+  [i,j]=ind2sub([n,n],edges(e)); % node indices of edge e  
+  EdgeL(e,:)=[idx(i) idx(j) adj(i,j)];
+end

test/test_poly_plot.m

@@ -3,7 +3,7 @@
 
 % Apply a filter
 N = matrix_normalize(A);
-c = filter_jackson(moments_delta(0.5, 500));
+c = filter_jackson(moments_delta(-0.5, 500));
 c(1) = c(1)/2;
 pN = mfunc_cheb_poly(c,N);
 
@@ -18,22 +18,37 @@
 [lambda,I] = sort(diag(D), 'descend');
 V = V(:,I);
 if length(D) == nprobe
-  warning('Did not converge to desired threshold\n');
+    warning('Did not converge to desired threshold\n');
 end
 
 
 % Sort out and select top group by leverage on the subspace
 nmode = sum(lambda > 0.95*lambda(1));
 score = sum(V(:,1:nmode).^2, 2);
 [sscore,I] = sort(score, 'descend');
-I = I(1:400);
 
+% Extract top  + one-hop neighborhood
+Nhi = 40;
+I = I(1:Nhi);
+for hop=1:2
+    z = sum(A(:,I),2);
+    z(I) = 1;
+    I = find(z > 0);
+end
+
+%I = I(1:800);
+sscore=sscore(I);
+As = A(I,I);
+EdgeL=adj2gephilab('test',As,sscore);
+
+%{
 % Plot top group
 figure;
 As = A(I,I);
 gplot_shatter(As,4);
+%}
 
-
+%{
 % Alternative: Heuristic sparsification
 marks = zeros(length(N),1);
 nmode = sum(lambda > 0.95*lambda(1));
@@ -43,3 +58,4 @@
 I = I(1:400);
 figure;
 gplot_shatter(A(I,I),4);
+%}

test/test_poly_plot.m~

@@ -0,0 +1,69 @@
+% Load one of the Gleich examples
+A = load_graph('gleich', 'pgp-cc');
+
+% Apply a filter
+N = matrix_normalize(A);
+c = filter_jackson(moments_delta(-0.5, 500));
+c(1) = c(1)/2;
+pN = mfunc_cheb_poly(c,N);
+
+% Pull out representative vectors (should revisit)
+nprobe = 200;
+thresh = 1e-3;
+Z = randn(length(N),nprobe);
+AZ = pN(Z);
+
+% Version 0: Eigenvector computation
+[V,D] = eig_rand1(Z, AZ, thresh);
+[lambda,I] = sort(diag(D), 'descend');
+V = V(:,I);
+if length(D) == nprobe
+    warning('Did not converge to desired threshold\n');
+end
+
+
+% Sort out and select top group by leverage on the subspace
+nmode = sum(lambda > 0.95*lambda(1));
+score = sum(V(:,1:nmode).^2, 2);
+[sscore,I] = sort(score, 'descend');
+
+% Extract top  + one-hop neighborhood
+Nhi = 40;
+I = I(1:Nhi);
+for hop=1:2
+    z = sum(A(:,I),2);
+    z(I) = 1;
+    I = find(z > 0);
+end
+
+%I = I(1:800);
+sscore=sscore(I);
+
+%Get it to gephi
+As = A(I,I);
+EdgeL=adj2gephilab('test',As,sscore);
+EdgeL2=adj2gephilab2('test2',As,sscore,I);
+
+
+%Get it back from gephi
+Q=csvread('export_text.csv',1,0);
+Ns=full(N(Q,Q));
+
+%{
+% Plot top group
+figure;
+As = A(I,I);
+gplot_shatter(As,4);
+%}
+
+%{
+% Alternative: Heuristic sparsification
+marks = zeros(length(N),1);
+nmode = sum(lambda > 0.95*lambda(1));
+[Q,R,E] = qr(V',0);
+VQ = V*Q';
+[~,I] = sort(max(abs(VQ(:,j)),[],2), 'descend');
+I = I(1:400);
+figure;
+gplot_shatter(A(I,I),4);
+%}