复杂网络聚类系数和平均路径长度计算的MATLAB源代码.doc
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复杂网络聚类系数和平均路径长度计算的MATLAB源代码.doc
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复杂网络聚类系数和平均路径长度计算的MATLAB源代码
申明:
文章来自百度用户carrot_hy
复杂网络的代码总共是三个m文件,复制如下:
第一个文件,CCM_ClusteringCoef.m
function[Cp_Global,Cp_Nodal]=CCM_ClusteringCoef(gMatrix,Types)
%CCM_ClusteringCoefcalculatesclusteringcoefficients.
%Input:
% gMatrix adjacencymatrix
% Types typeofgraph:
'binary','weighted','directed','all'(default).
%Usage:
% [Cp_Global,Cp_Nodal]=CCM_ClusteringCoef(gMatrix,Types)returns
% clusteringcoefficientsforallnodes"Cp_Nodal"andaverageclustering
% coefficientofnetwork"Cp_Global".
%Example:
% G=CCM_TestGraph1('nograph');
% [Cp_Global,Cp_Nodal]=CCM_ClusteringCoef(G);
%Note:
% 1)onenodehavevaule0,whilewhichonlyhasaneighbourornone.
% 2)Thedirctednetworktermedtripletsthatfulfillthefollowcondition
% asnon-vacuous:
j->i->kandk->i-j,ifdon'tsatisfywiththatas
% vacuous,justlike:
j->i,k->iandi->j,i->k.andtheclosedtriplets
% onlyj->i->k==j->kandk->i->j==k->j.
% 3)'ALL'typenetworkcodefromMikaRubinov'sBCTtoolkit.
%Refer:
% [1]Barratetal.(2004)Thearchitectureofthecomplexweightednetworks.
% [2]Wasserman,S.,Faust,K.(1994)SocialNetworkAnalysis:
Methodsand
% Applications.
% [3]ToreOpsahlandPietroPanzarasa(2009)."ClusteringinWeighted
% Networks".SocialNetworks31
(2).
%SeealsoCCM_Transitivity
%WrittenbyYongLiu,Oct,2007
%CenterforComputationalMedicine(CCM),
%NationalLaboratoryofPatternRecognition(NLPR),
%InstituteofAutomation,ChineseAcademyofSciences(IACAS),China.
%RevisebyHuYong,Nov,2010
%E-mail:
%basedonMatlab2006a
%$Revision:
1.0,Copywrite(c)2007
error(nargchk(1,2,nargin,'struct'));
if(nargin<2), Types='all'; end
N=length(gMatrix);
gMatrix(1:
(N+1):
end)=0;%Clearself-edges
Cp_Nodal=zeros(N,1); %Preallocate
switch(upper(Types))
case'BINARY'%Binarynetwork
gMatrix=double(gMatrix>0);%Ensurebinarynetwork
fori=1:
N
neighbor=(gMatrix(i,:
)>0);
Num =sum(neighbor);%numberofneighbornodes
temp =gMatrix(neighbor,neighbor);
if(Num>1), Cp_Nodal(i)=sum(temp(:
))/Num/(Num-1); end
end
case'WEIGHTED'%Weightednetwork--arithmeticmean
fori=1:
N
neighbor=(gMatrix(i,:
)>0);
n_weight=gMatrix(i,neighbor);
Si =sum(n_weight);
Num =sum(neighbor);
if(Num>1),
n_weight =ones(Num,1)*n_weight;
n_weight =n_weight+n_weight';
n_weight =n_weight.*(gMatrix(neighbor,neighbor)>0);
Cp_Nodal(i)=sum(n_weight(:
))/(2*Si*(Num-1));
end
end
%case'WEIGHTED'%Weightednetwork--geometricmean
% A =(gMatrix~=0);
% G3=diag((gMatrix.^(1/3))^3);)
% A(A==0)=inf; %close-tripletnoexist,letCpNode=0(A=inf)
% CpNode=G3./(A.*(A-1));
case'DIRECTED',%Directednetwork
fori=1:
N
inset =(gMatrix(:
i)>0); %in-nodesset
outset =(gMatrix(i,:
)>0)';%out-nodesset
if(any(inset&outset))
allset=and(inset,outset);
%Ensureaji*aik>0,jbelongstoinset,andkbelongstooutset
total =sum(inset)*sum(outset)-sum(allset);
tri =sum(sum(gMatrix(inset,outset)));
Cp_Nodal(i)=tri./total;
end
end
%case'DIRECTED',%Directednetwork--clarityformat(fromMikaRubinov,UNSW)
% G =gMatrix+gMatrix'; %symmetrized
% D =sum(G,2); %totaldegree
% g3=diag(G^3)/2; %numberoftriplet
% D(g3==0)=inf; %3-cyclesnoexist,letCp=0
% c3=D.*(D-1)-2*diag(gMatrix^2);%numberofallpossible3-cycles
% Cp_Nodal =g3./c3;
%Note:
Directed&weightednetwork(fromMikaRubinov)
case'ALL',%Alltype
A =(gMatrix~=0); %adjacencymatrix
G =gMatrix.^(1/3)+(gMatrix.').^(1/3);
D =sum(A+A.',2); %totaldegree
g3=diag(G^3)/2; %numberoftriplet
D(g3==0)=inf; %3-cyclesnoexist,letCp=0
c3=D.*(D-1)-2*diag(A^2);
Cp_Nodal =g3./c3;
otherwise,%EorrMsg
error('Typeonlyfour:
"Binary","Weighted","Directed",and"All"');
end
Cp_Global=sum(Cp_Nodal)/N;
%%
第二个文件:
CCM_AvgShortestPath.m
function[D_Global,D_Nodal]=CCM_AvgShortestPath(gMatrix,s,t)
%CCM_AvgShortestPathgeneratestheshortestdistancematrixofsourcenodes
%indicestothetargetnodesindicet.
%Input:
% gMatrix symmetrybinaryconnectmatrixorweightedconnectmatrix
% s sourcenodes,defaultis1:
N
% t targetnodes,defaultis1:
N
%Usage:
% [D_Global,D_Nodal]=CCM_AvgShortestPath(gMatrix)returnsthemean
% shortest-pathlengthofwholenetworkD_Global,and
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