get_clustering_coefficient.m

复杂网络complex netowrk 基本参数 聚类系数计算

function cc=get_clustering_coefficient(Nodes,N,nodes_attribute)
%This progrom is to calculate the clustering coefficient of graph in Nodes.
%The clustering coefficient is defined in Newman Review[2003] IIIB.Eq.(5)(6)
%Ci=number of triangles connedted to vertex i/number of triples contered on vertex i
%C=sum(Ci)/n

%Input:Nodes--N*N matrix, saving the information graph with adjacent matrix
%             if nodes_attribute=1, or adjacent list if nodes_attribute=2.
%             Default value is nodes_attribute=1.NO Care of Nodes(i,i)
%      N--the number of node in Nodes
%      nodes_attribute--the attribute of Nodes,nodes_attribute=1 if Nodes is
%                      adjacent matrix,nodes_attribute=2 if it is adjacent
%                      list. The default value is nodes_attribute=1 
%Output:cc--reture the clustering coefficient of Nodes
 
%clear



%Add sparse matrix on 05/08/16
%TEST_FULL_SPARAE=1 is full matrix,TEST_FULL_SPARAE=2 is sparse matrix,
TEST_FULL_SPARAE=1;

if nodes_attribute==1
    if TEST_FULL_SPARAE==1
        Node_CC=zeros(N,1);
    elseif TEST_FULL_SPARAE==2%Add sparse matrix on 05/08/16
        Nodes_CC=sparse(N,1);
    end
    
    for i=1:N %calculate the CC for each node 
        triangle_num=0;
        Neighbor=find(Nodes(i,:)>0);
        node_degree=nnz(Neighbor);
        if node_degree==0||node_degree==1
            Node_CC(i)=0;              
        else
            triple_num=node_degree*(node_degree-1)/2;
            for j=1:(node_degree-1)%neighbor 1
                for k=(j+1):node_degree%neighbor 2
                    if Nodes(Neighbor(j),Neighbor(k))>0
                        triangle_num=triangle_num+1;
                    end
                end
            end%j=1:node_degree
            Node_CC(i)=triangle_num/triple_num;
        end%node_degree        
    end%i    
    cc=sum(Node_CC)/N;
end%nodes_attribute

if TEST==1;
    t=toc
    cc
end

return