性能 – 是否有最有效的方法来为Avg Clustering Coeff编写程序

计算图的平均聚类系数
我得到了正确的结果,但是图形尺寸增加需要一些替代方法需要花费大量时间,因此执行时间较短.有没有办法简化代码？

%// A is adjacency matrix N X N, 
    %// d is degree ,

    N=100;
    d=10;
    rand('state',0)
    A = zeros(N,N);
    kv=d*(d-1)/2; 

%% Creating A matrix %%%

for i = 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 %%

    for i=1:N   
        J=find(A(i,:));  
        et=0;
        for ii=1:(size(J,2))-1
            for 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;

输出我得到了.

Avg_clustering_coeff = 0.1107

可以使用 nchoosek对聚类Coeff部分的计算进行矢量化以移除最里面的两个嵌套循环,如下所示 –

CvOut = zeros(1,N);
for 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;

希望这会提升性能相当多！