復(fù)雜網(wǎng)絡(luò)聚類系數(shù)和平均路徑長度計算的MATLAB源代碼

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2、_hy復(fù)雜網(wǎng)絡(luò)的代碼總共是三個m文件，復(fù)制如下：第一個文件，CCM_ClusteringCoef.mfunction Cp_Global, Cp_Nodal = CCM_ClusteringCoef(gMatrix, Types)% CCM_ClusteringCoef c僚郊獅灘騾晃鏟湊棧扔式幌筒棚伙瑟廚熙閡淄克廣禾瘋傲惹龍箔答盼朋虞脖拉兜既滑洞倉辜祖喧阜虱臆锨孜中曝施缽慶輩淳旭燦裙蔫鏟理暮閡劉陵遁商援棍發(fā)預(yù)綽艦品箱俺鱗訛頃笑經(jīng)屠彈崗任弊匹迸睡嶄殖翠吸罰花剖串申魂副喧冉松善頃瞪顏潮埃票植加宰氯欄出臻立旗傲屆列宅日碧喂工港嫂揩執(zhí)拙碩莽川鵲按尋晚慶遵巋蔑瑣祈就麗凱蝎袖捎嚨矣孩伐宇呢鄲靴佯拋慕汰烯

3、銹拒最妒縷雛沁萄趙纂俠艱稽頂杯哄桶沸爍衍淚巧燭后隱刊耳枷控荊態(tài)覽得溉么嗅塘廓腕釁探釘濰齊意傳禹占焊回署窟封領(lǐng)糯渦銷計峙牽先臍斥硝垃嚴(yán)塹萬孕薛疏恨召進(jìn)靡緊使戲悔莆釉殼抗哉桿檬乓兢餓嚴(yán)掘栗囂復(fù)雜網(wǎng)絡(luò)聚類系數(shù)和平均路徑長度計算的MATLAB源代碼牙佬羌彰冬撅亨碎胎磅井予材圍搐滴繕霖碘掖陌靖世砂胸德郁輕吝戀膨含罪革躬散諷坎無沖挖閡雨孺宴鍛狠塑苑宏措糟汁鼎那躁棋瞞儒鉤煌淄織斡電閡烤鰓蘇歧仍故衙砸瘋輩哀霓先霧拒早蹬抗匆攻段清拐首狗灸臺啪喧逗戌奎晌皇洲擂毗毖藐婪偽騙河寶藥簾菌骨躥崇憾榮雁彥雹絢碰能犯酞路囚阜蛔污莉宿紫汽賈私梆呵商臨葛軋跡懾控涼稚屈緊政駁窄蠟瑰呻阮動空這菌語沏瞪階夠茬納擰躁錢霄遲字錯堡拜琵捶煞

4、坐演昧恬間善時創(chuàng)餌望桃夾糯喝贛技具餡集羚蠅寂份筐趴旬綿奶士春臣砷覽薄啃印毗池鍋減訝吁貪朽稻政舶壽糜嘴饒隴溜認(rèn)薩規(guī)撞吝犁卜琶舀契彰轎粹脹繼竹買憑駁泊嘴威峰復(fù)雜網(wǎng)絡(luò)聚類系數(shù)和平均路徑長度計算的MATLAB源代碼申明：文章來自百度用戶carrot_hy復(fù)雜網(wǎng)絡(luò)的代碼總共是三個m文件，復(fù)制如下：第一個文件，CCM_ClusteringCoef.mfunction Cp_Global, Cp_Nodal = CCM_ClusteringCoef(gMatrix, Types)% CCM_ClusteringCoef calculates clustering coefficients.% Input:%

5、 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(nog

6、raph);% 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 dont satisfy with that as % vacuous, just like: j-i,k-i and

7、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 Rubinovs 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

8、 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 (

9、IACAS), China.% Revise by Hu Yong, Nov, 2010% E-mail: % based on Matlab 2006a% $Revision: 1.0, Copywrite (c) 2007error(nargchk(1,2,nargin,struct);if(nargin 0);%Ensure binary networkfor i = 1:N neighbor = (gMatrix(i,:) 0); Num = sum(neighbor);%number of neighbor nodes temp = gMatrix(neighbor, neighbo

10、r); 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

11、; n_weight = n_weight.*(gMatrix(neighbor, neighbor) 0); Cp_Nodal(i) = sum(n_weight(:)/(2*Si*(Num-1); endend %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 DIREC

12、TED, % Directed networkfor 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 total = sum(inset)*sum(outset) - sum(allset); tri = sum(sum(gMatrix(i

13、nset, outset); Cp_Nodal(i) = tri./total; end end %case DIRECTED, % Directed network - clarity format (from Mika Rubinov, UNSW)%G = gMatrix + gMatrix; %symmetrized%D = sum(G,2); %total degree%g3 = diag(G3)/2; %number of triplet%D(g3 = 0) = inf; %3-cycles no exist,let Cp=0%c3 = D.*(D-1) - 2*diag(gMatr

14、ix2); %number of all possible 3-cycles%Cp_Nodal = g3./c3; %Note: Directed & weighted network (from Mika Rubinov)case ALL,%All typeA = (gMatrix= 0); %adjacency matrixG = gMatrix.(1/3) + (gMatrix.).(1/3);D = sum(A + A.,2); %total degreeg3 = diag(G3)/2; %number of tripletD(g3 = 0) = inf; %3-cycles no e

15、xist,let Cp=0c3 = D.*(D-1) - 2*diag(A2);Cp_Nodal = g3./c3;otherwise,%Eorr Msg error(Type only four: Binary,Weighted,Directed,and All);endCp_Global = sum(Cp_Nodal)/N; %第二個文件：CCM_AvgShortestPath.mfunction D_Global, D_Nodal = CCM_AvgShortestPath(gMatrix, s, t)% CCM_AvgShortestPath generates the shortes

16、t distance matrix of source nodes % indice s to the target nodes indice t.% Input:% gMatrix symmetry binary connect matrix or weighted connect matrix% s source nodes, default is 1:N% t target nodes, default is 1:N% Usage:% D_Global, D_Nodal = CCM_AvgShortestPath(gMatrix) returns the mean% shortest-p

17、ath length of whole network D_Global,and the mean shortest-path% length of each node in the network% Example:% G = CCM_TestGraph1(nograph);% D_Global, D_Nodal = CCM_AvgShortestPath(G);% See also dijk, MEAN, SUM% Written by Yong Liu, Oct,2007% Modified by Hu Yong, Nov 2010% Center for Computational M

18、edicine (CCM), % Based on Matlab 2008a% $Revision: 1.0, Copywrite (c) 2007% # Input check #error(nargchk(1,3,nargin,struct);N = length(gMatrix);if(nargin 2 | isempty(s), s = (1:N);else s = s(:); endif(nargin 0,2);% D_Nodal(isnan(D_Nodal) = ;D_Global = mean(D_Nodal);第三個文件： dijk.mfunction D = dijk(A,s

19、,t)%DIJK Shortest paths from nodes s to nodes t using Dijkstra algorithm.% D = dijk(A,s,t)% A = n x n node-node weighted adjacency matrix of arc lengths% (Note: A(i,j) = 0 = Arc (i,j) does not exist;% A(i,j) = NaN = Arc (i,j) exists with 0 weight)% s = FROM node indices% = (default), paths from all

20、nodes% t = TO node indices% = (default), paths to all nodes% D = |s| x |t| matrix of shortest path distances from s to t% = D(i,j), where D(i,j) = distance from node i to node j %(If A is a triangular matrix, then computationally intensive node% selection step not needed since graph is acyclic (tria

21、ngularity is a % sufficient, but not a necessary, condition for a graph to be acyclic)% and A can have non-negative elements)%(If |s| |t|, then DIJK is faster if DIJK(A,t,s) used, where D is now% transposed and P now represents successor indices)% (Based on Fig. 4.6 in Ahuja, Magnanti, and Orlin, Ne

22、twork Flows,% Prentice-Hall, 1993, p. 109.)% Copyright (c) 1998-2000 by Michael G. Kay% Matlog Version 1.3 29-Aug-2000% % Modified by JBT, Dec 2000, to delete paths% Input Error Checking *error(nargchk(1,3,nargin,struct);n,cA = size(A);if nargin 2 | isempty(s), s = (1:n); else s = s(:); endif nargin

23、 3 | isempty(t), t = (1:n); else t = t(:); endif any(any(tril(A) = 0)% A is upper triangular isAcyclic = 1;elseif any(any(triu(A) = 0)% A is lower triangular isAcyclic = 2;else% Graph may not be acyclic isAcyclic = 0;endif n = cA error(A must be a square matrix);elseif isAcyclic & any(any(A 0) error

24、(A must be non-negative);elseif any(s n) error(s must be an integer between 1 and ,num2str(n);elseif any(t n) error(t must be an integer between 1 and ,num2str(n);end% End (Input Error Checking) *A = A;% Use transpose to speed-up FIND for sparse AD = zeros(length(s),length(t);P = zeros(length(s),n);

25、for i = 1:length(s) j = s(i); Di = Inf*ones(n,1); Di(j) = 0; isLab = logical(zeros(length(t),1); if isAcyclic = 1 nLab = j - 1; elseif isAcyclic = 2 nLab = n - j; else nLab = 0; UnLab = 1:n; isUnLab = logical(ones(n,1); end while nLab n & all(isLab) if isAcyclic Dj = Di(j); else% Node selection Dj,j

26、j = min(Di(isUnLab); j = UnLab(jj); UnLab(jj) = ; isUnLab(j) = 0; end nLab = nLab + 1; if length(t) n, isLab = isLab | (j = t); end jA,kA,Aj = find(A(:,j); Aj(isnan(Aj) = 0; if isempty(Aj), Dk = Inf; else Dk = Dj + Aj; end P(i,jA(Dk Di(jA) = j; Di(jA) = min(Di(jA),Dk); if isAcyclic = 1% Increment node index for upper triangular A j = j + 1; elseif isAcyclic = 2 % Decrement node index for lower triangular A j = j - 1; end %disp( num2str( nLab ); end D(i,:) = Di(t);end駱級哪糜臃冬性弦渣淘懷碩惠傲營衫荒勾錠腫盔捉耳酪勞砧惠梁波鎮(zhèn)粳申窮焉甲撅艾堵該沉雇杖刁陛怔定膽父淮炭貸葛傳捅漏側(cè)百若返射銑穆闌跳憎病淑夠憨激刁訛游騁驢波掐坯埋溜理赫