performance - Is there most efficient way to code program for Avg Clustering Coeff -

calculation of average clustering coefficient of graph getting correct result takes huge time when graph dimension increases need alternative way takes less time execute. there way simplify code??

    %// adjacency matrix n x n,      %// d degree ,      n=100;     d=10;     rand('state',0)     = zeros(n,n);     kv=d*(d-1)/2;   %% creating matrix %%%  = 1:(d*n/2)     j = floor(n*rand)+1;     k = floor(n*rand)+1;     while (j==k)||(a(j,k)==1)         j = floor(n*rand)+1;         k = floor(n*rand)+1;     end     a(j,k)=1;     a(k,j)=1; end   %%   calculation of clustering coeff %%      i=1:n            j=find(a(i,:));           et=0;         ii=1:(size(j,2))-1             jj=ii+1:size(j,2)                             et=et+a(j(ii),j(jj));             end         end         cv(i)=et/kv;     end     avg_clustering_coeff=sum(cv)/n; 

output got.

avg_clustering_coeff = 0.1107

that calculation of clustering coeff part vectorized using nchoosek remove innermost 2 nested loops, -

cvout = zeros(1,n); k=1:n     j=find(a(k,:));     if numel(j)>1         idx = nchoosek(j,2);         cvout(k) = sum(a(sub2ind([n n],idx(:,1),idx(:,2))));     end end cvout=cvout/kv; 

hopefully, boost performance quite bit!