复杂网络聚类系数和平均路径长度计算的MATLAB源代码

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_ClusteringCoef calculates clustering coefficients.

% Input:

% gMatrix adjacency matrix

% Types type of graph:

'binary','weighted','directed','all'(default).

% Usage:

% [Cp_Global, Cp_Nodal] = CCM_ClusteringCoef(gMatrix, Types) returns

% clustering coefficients for all nodes "Cp_Nodal" and average clustering

% coefficient of network "Cp_Global".

% Example:

% G = CCM_TestGraph1('nograph');

% [Cp_Global, Cp_Nodal] = CCM_ClusteringCoef(G);

% Note:

% 1) one node have vaule 0, while which only has a neighbour or none.

% 2) The dircted network termed triplets that fulfill the follow condition % as non-vacuous: j->i->k and k->i-j,if don't satisfy with that as

% vacuous, just like: j->i,k->i and i->j,i->k. and the closed triplets % only j->i->k == j->k and k->i->j == k->j.

% 3) 'ALL' type network code from Mika Rubinov's BCT toolkit.

% Refer:

% [1] Barrat et al. (2004) The architecture of the complex weighted networks. % [2] Wasserman,S.,Faust,K.(1994) Social Network Analysis: Methods and

% Applications.

% [3] Tore Opsahl and Pietro Panzarasa (2009). "Clustering in Weighted

% Networks". Social Networks31(2).

% See also CCM_Transitivity

% Written by Yong Liu, Oct,2007

% Center for Computational Medicine (CCM),

% National Laboratory of Pattern Recognition (NLPR),

% Institute of Automation,Chinese Academy of Sciences (IACAS), China.

% Revise by Hu Yong, Nov, 2010

% E-mail:

% based on Matlab 2006a

% $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;%Clear self-edges

Cp_Nodal = zeros(N,1); %Preallocate

switch(upper(Types))

case 'BINARY'%Binary network

gMatrix = double(gMatrix > 0);%Ensure binary network

for i = 1:N

neighbor = (gMatrix(i,:) > 0);

Num = sum(neighbor);%number of neighbor nodes

temp = gMatrix(neighbor, neighbor);

if(Num > 1), Cp_Nodal(i) = sum(temp(:))/Num/(Num-1); end end

case 'WEIGHTED'% Weighted network -- arithmetic mean

for i = 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'% Weighted network -- geometric mean

% A = (gMatrix~= 0);

% G3 = diag((gMatrix.^(1/3) )^3);)

% A(A == 0) = inf; %close-triplet no exist,let CpNode=0 (A=inf)

% CpNode = G3./(A.*(A-1));

case 'DIRECTED', % Directed network

for i = 1:N

inset = (gMatrix(:,i) > 0); %in-nodes set

outset = (gMatrix(i,:) > 0)'; %out-nodes set

if(any(inset & outset))

allset = and(inset, outset);

% Ensure aji*aik > 0,j belongs to inset,and k belongs to outset