54 lines
1.2 KiB
Matlab
54 lines
1.2 KiB
Matlab
% Weighted clustering coefficient (edge-weights).
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% Source: Barrat et al, The architecture of complex weighted networks.
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%
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% INPUTS: weighted adjacency matrix, nxn
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% OUTPUTs: vector of node weighted clustering coefficients, nx1
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%
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% Other routines used: degrees.m, kneighbors.m
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% GB: last updated, Sep 30 2012
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function wC=weightedClustCoeff(adj)
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[deg,~,~]=degrees(adj);
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n=size(adj,1); % number of nodes
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wC=zeros(n,1); % initialize weighted clust coeff
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for i=1:n % across all nodes
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neigh=kneighbors(adj,i,1);
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if length(neigh)<2; continue; end
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s=0;
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for ii=1:length(neigh)
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for jj=1:length(neigh)
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if adj(neigh(ii),neigh(jj))>0; s=s+(adj(i,neigh(ii))+adj(i,neigh(jj)))/2; end
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end
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end
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wC(i)=s/(deg(i)*(length(neigh)-1));
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end
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% % ALTERNATIVE ...........................
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% wadj=adj;
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% adj=adj>0;
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%
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% [wdeg,~,~]=degrees(wadj);
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% [deg,~,~]=degrees(adj);
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% n=size(adj,1); % number of nodes
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% wC=zeros(n,1);
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%
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% for i=1:n
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% if deg(i)<2; continue; end
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%
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% s=0;
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% for ii=1:n
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% for jj=1:n
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% s=s+adj(i,ii)*adj(i,jj)*adj(ii,jj)*(wadj(i,ii)+wadj(i,jj))/2;
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% end
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% end
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%
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% wC(i)=s/(wdeg(i)*(deg(i)-1));
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% end
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% .......................................... |