Authorship added

davegrays · davegrays · commit 37dd9e9e5021 · 2015-12-03T01:56:06.000-08:00
diff --git a/closeness_bin.m b/closeness_bin.m
@@ -2,7 +2,9 @@
 %CLOSENESS_CENTRALITY     closeness centrality.
 %
 %   [ccen] = closeness_bin(CIJ)
+%
+% -David Grayson 2015
 
 N=size(CIJ,1)-1;
 D=distance_bin(CIJ);
-ccen=N./sum(D);
+ccen=N./sum(D);
diff --git a/closeness_wei.m b/closeness_wei.m
@@ -2,11 +2,13 @@
 %CLOSENESS_CENTRALITY     closeness centrality.
 %
 %   [ccen] = closeness_wei(CIJ)
+%
+% -David Grayson 2014
 
 n=size(CIJ,1);
 ind = CIJ~=0;
 CIJ(ind) = 1./CIJ(ind);                             %connection-length matrix
 
 D=distance_wei(CIJ);
 D(1:n+1:end)=0;              %set diagonal to 0
-ccen=(n-1)./(sum(D)/2);
+ccen=(n-1)./(sum(D)/2);
diff --git a/communicability_bin.m b/communicability_bin.m
@@ -19,6 +19,8 @@
 %
 %   Output:     Cglob,          global communicability (scalar)
 %               Cloc,           local communicability (vector)
+%
+% -David Grayson 2014
 
 n = size(A,1);
 
@@ -32,4 +34,4 @@
 else
     G(1:n+1:end) = 0;           %set diagonal to 0
     C=sum(G(:))/(n*(n-1));    %total communicability (scalar)
-end
+end
diff --git a/communicability_centrality_bin.m b/communicability_centrality_bin.m
@@ -10,6 +10,8 @@
 %
 %   A must be a binary undirected or directed adjacency matrix
 %   CBC will be a row vector of the communicability centralities of nodes
+%
+% -David Grayson 2014
 
 n = size(A,1);
 oneu=triu(ones(n,n),1);
diff --git a/communicability_centrality_wei.m b/communicability_centrality_wei.m
@@ -10,6 +10,8 @@
 %
 %   W must be a weighted undirected or directed adjacency matrix
 %   CBC will be a row vector of the communicability centralities of nodes
+%
+% -David Grayson 2014
 
 n = size(W,1);
 oneu=triu(ones(n,n),1);
diff --git a/communicability_wei.m b/communicability_wei.m
@@ -19,6 +19,8 @@
 %
 %   Output:     Cglob,          global communicability (scalar)
 %               Cloc,           local communicability (vector)
+%
+% -David Grayson 2014
 
 n = size(W,1);
 
@@ -32,4 +34,4 @@
 else
     G(1:n+1:end) = 0;           %set diagonal to 0
     C=sum(G(:))/(n*(n-1));    %total communicability (scalar)
-end
+end
diff --git a/compute_modularity.m b/compute_modularity.m
@@ -2,6 +2,8 @@
 %COMPUTE_MODULARITY     computes Q from input matrix and community vector.
 %
 %   Q = compute_modularity(W,ci)
+%
+% -David Grayson 2014
 
 N=length(W);
 K=sum(W);                        %degree
diff --git a/convertMAT3D_MYcom_wei.m b/convertMAT3D_MYcom_wei.m
@@ -6,15 +6,16 @@
 %
 %   Inputs:
 %   - subject_array_3D is a 3D matrix of individual 2D weighted
-%   undirected matrices with subjects in 3rd dimension
+%   (un)directed matrices with subjects in 3rd dimension
 %   diagonal of 2D matrices should be 0
 %   - order means you want to include walks up to what length (1 is
 %   monosynaptic, 2 is disynaptic, 3 trisynaptic, etc.)
 %   - af is attenuation factor: the penalty factor by which you multiply
 %   for every additional step taken (0<af<1)
 %
 %   Example: G_3D = convertMAT3D_MYcom_wei(subject_array_3D,5,.5);
-
+%
+% -David Grayson 2014
 
 slen=size(subject_array_3D,3);
 sx=size(subject_array_3D,1);
@@ -30,4 +31,4 @@
     end
 
     COM_3D(:,:,s)=G;
-end
+end
diff --git a/convertMAT3D_SI_und.m b/convertMAT3D_SI_und.m
@@ -6,6 +6,8 @@
 % subject_array_3D. converts them to search information matrices.
 % based on methods in Goni 2014 PNAS: Resting-brain functional connectivity
 % predicted by analytic measures of network communication
+%
+% -David Grayson 2014
 
 slen=size(subject_array_3D,3);
 
@@ -44,4 +46,4 @@
     
     %% add to 3D matrix
     SI_3D(:,:,su)=Gsi;
-end
+end
diff --git a/convertMAT3D_com_bin.m b/convertMAT3D_com_bin.m
@@ -6,9 +6,10 @@
 %
 %   Inputs:
 %   subject_array_3D is a 3D matrix of individual 2D binary
-%   undirected matrices with subjects in 3rd dimension
+%   (un)directed matrices with subjects in 3rd dimension
 %   diagonal of 2D matrices should be 0
-
+%
+% -David Grayson 2014
 
 slen=size(subject_array_3D,3);
 
@@ -17,4 +18,4 @@
     G=expm(W);            %communicability matrix
 
     COM_3D(:,:,s)=G;
-end
+end
diff --git a/convertMAT3D_com_wei.m b/convertMAT3D_com_wei.m
@@ -6,9 +6,10 @@
 %
 %   Inputs:
 %   subject_array_3D is a 3D matrix of individual 2D weighted
-%   undirected matrices with subjects in 3rd dimension
+%   (un)directed matrices with subjects in 3rd dimension
 %   diagonal of 2D matrices should be 0
-
+%
+% -David Grayson 2014
 
 slen=size(subject_array_3D,3);
 
@@ -17,4 +18,4 @@
     G=expm(W./sqrt(sum(W)'*sum(W)));            %communicability matrix
 
     COM_3D(:,:,s)=G;
-end
+end
diff --git a/convertMAT3D_disinv_wei.m b/convertMAT3D_disinv_wei.m
@@ -8,7 +8,8 @@
 %   subject_array_3D is a 3D matrix of individual 2D weighted
 %   undirected matrices with subjects in 3rd dimension
 %   diagonal of 2D matrices should be 0
-
+%
+% -David Grayson 2014
 
 slen=size(subject_array_3D,3);
 n=size(subject_array_3D,1);
@@ -22,4 +23,4 @@
     D=1./D;                       %invert distance
     D(1:n+1:end)=0;              %set diagonal to 0
     DIS_3D(:,:,s)=D;
-end
+end
diff --git a/convertMAT3D_mfpt_und.m b/convertMAT3D_mfpt_und.m
@@ -5,9 +5,10 @@
 %
 %   Inputs:
 %   subject_array_3D is a 3D matrix of individual 2D binary/weighted
-%   undirected matrices with subjects in 3rd dimension
+%   (un)directed matrices with subjects in 3rd dimension
 %   diagonal of 2D matrices should be 0
-
+%
+% -David Grayson 2014
 
 slen=size(subject_array_3D,3);
 N = size(subject_array_3D,1);
@@ -74,4 +75,4 @@
                 e];
             sspi = A\B;
             ETij = 1/sspi(j);
-        end
+        end
diff --git a/graphtheory_glob_3D.m b/graphtheory_glob_3D.m
@@ -22,6 +22,8 @@
 %the size of the input matrix M's 3rd dimension.
 %G_METRICS is a cell array listing the names of the metrics for
 %corresponding columns of Gout
+%
+% -David Grayson 2014
 
 if strcmp(Mtype,'wei')
     for s=1:size(M,3)
@@ -112,4 +114,4 @@
 if exist('mfpt','var') && mfpt
     g=g+1;G_METRICS{1,g}='mfpt';
 end
-G_METRICS{2,1}=Mtype;
+G_METRICS{2,1}=Mtype;
diff --git a/graphtheory_loc_3D.m b/graphtheory_loc_3D.m
@@ -23,6 +23,8 @@
 %dimension
 %G_METRICS is a cell array listing the names of the metrics for
 %corresponding columns of Gout
+%
+% -David Grayson 2014
 
 if strcmp(Mtype,'wei')
     for s=1:size(M,3)
diff --git a/ipsi.m b/ipsi.m
@@ -15,6 +15,8 @@
 % hom is array of homotopic connection weights (length N/2)
 % contras_l and contras_r are total contralateral connectivity, including
 % homotopic, for left and right hemi nodes respectively (length N/2)
+%
+% -David Grayson 2014
 
 slen=size(subject_array_3D,3);
 
@@ -40,4 +42,4 @@
     hom_3D(s,:)=hom;
     contras_l_3D(s,:)=contras_l;
     contras_r_3D(s,:)=contras_r;
-end
+end
diff --git a/laplacian_und.m b/laplacian_und.m
@@ -6,6 +6,8 @@
 % this function only considers undirected binary/weighted matrices with 0 diagonal.
 % computes laplacian centrality of each node
 % outputs lcen (vector)
+%
+% -David Grayson 2014
 
 m=size(CIJ,1);
 CIJ(1:m+1:end)=0; %ensure diagonal is 0
@@ -25,4 +27,4 @@
     LEi(i)=sum(eig(Li).^2); %laplacian energy for node removed
 end
 
-lcen=(LE-LEi)/LE; %laplacian centrality
+lcen=(LE-LEi)/LE; %laplacian centrality
diff --git a/matrices2svdscores.m b/matrices2svdscores.m
@@ -19,6 +19,7 @@
 %
 %   outputs will be arranged in order of ascending scores
 %
+% -David Grayson 2014
 
 %% convert 2D mats stacked on 3rd dim to 1D mats stacked on 2nd dim
 mlen=size(m3d,1); %2D matrix size
diff --git a/matthresh_3D.m b/matthresh_3D.m
@@ -10,6 +10,8 @@
 %(ttype='dens'), total weight fraction (ttype='twf'), or individual
 %connection weight (ttype='weight'). minimum density must also be expressed 
 %as a fraction (0-1)
+%
+% -David Grayson 2014
 
 mlen=size(M,1); %2D matrix size
 slen=size(M,3); %#subjects
@@ -32,4 +34,4 @@
     end
     S(1:mlen+1:end)=0; %set diag to 0
     Mout(:,:,s)=S;
-end
+end
diff --git a/mfpt_und.m b/mfpt_und.m
@@ -18,6 +18,8 @@
 %
 %   Output:     Mglob,          global mfpt (scalar)
 %               Mloc,           local mfpt (vector)
+%
+% -David Grayson 2014
 
 N = size(W,1);
 oneu=triu(ones(N,N),1);
@@ -94,4 +96,4 @@
                 e];
             sspi = A\B;
             ETij = 1/sspi(j);
-        end
+        end
diff --git a/modularity_maxlike_estimator.m b/modularity_maxlike_estimator.m
@@ -2,6 +2,9 @@
 
 % get community structure using the methodology described in
 % Grayson et al 2014
+%
+% -David Grayson 2014
+
 Ci = zeros(iters,length(am));
 for i = 1:iters
     if finetune==1
diff --git a/patchline.m b/patchline.m
diff --git a/springembed.m b/springembed.m
diff --git a/tmpconvertMAT3D_com_wei.m b/tmpconvertMAT3D_com_wei.m
diff --git a/tmpconvertMAT3D_mfpt_und.m b/tmpconvertMAT3D_mfpt_und.m

Original file line number	Diff line number	Diff line change
`@@ -10,6 +10,8 @@`
`10`	`10`	`%`
`11`	`11`	`% A must be a binary undirected or directed adjacency matrix`
`12`	`12`	`% CBC will be a row vector of the communicability centralities of nodes`
	`13`	`+%`
	`14`	`+% -David Grayson 2014`
`13`	`15`
`14`	`16`	`n = size(A,1);`
`15`	`17`	`oneu=triu(ones(n,n),1);`
Original file line number	Diff line number	Diff line change
`@@ -2,6 +2,8 @@`
`2`	`2`	`%COMPUTE_MODULARITY computes Q from input matrix and community vector.`
`3`	`3`	`%`
`4`	`4`	`% Q = compute_modularity(W,ci)`
	`5`	`+%`
	`6`	`+% -David Grayson 2014`
`5`	`7`
`6`	`8`	`N=length(W);`
`7`	`9`	`K=sum(W); %degree`