-
Notifications
You must be signed in to change notification settings - Fork 1
/
A_NIDM.m
473 lines (429 loc) · 21.6 KB
/
A_NIDM.m
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
% % function [TableScores ] = A_NIDM(AdjSet, P0, aaa, aaa, aaa, aaa, aaa, aaa, IndividualNetRes )
function [TableScores ] = A_NIDM(AdjSet, P0, MDL, IndividualNetRes )
% % % % % % % % % % % % % % % % % % % %
% NormalizationType ='LaplacianNormalizationMeanDegree', 'ProbabilityNormalizationColumn', 'LaplacianNormalization'
% % if length( ID_dis )>1; error('The number of disease ids in ID_dis is larger than 1'); end
if ~exist('MDL','var') || isempty(MDL); MDL = 'M' ; end
if ~exist('IndividualNetRes','var') || isempty(IndividualNetRes); IndividualNetRes = false; end
tau_vec = [];
isdebug = false;
ImpulseRestart_Times = [ 1000 , 1 ]; % period and times
weight_inter_net = 1; %
DiffusionConstant = 1 ;
delta_t_input = 0.005;
IndividualNormalizationType = 'None' ; % Normalize each sub-network?
%
switch MDL
case 'M'; NormalizationType='LaplacianNormalizationMeanDegree';
case 'C'; NormalizationType='ProbabilityNormalizationColumn';
case 'R'; NormalizationType='ProbabilityNormalizationRow';
case 'L'; NormalizationType='LaplacianNormalization';
otherwise; error('There is no definition.');
end
% 控制脉冲时间周期；大于1 表示多久出现一次脉冲；若小于1 表示 脉冲出现的时间比，如0.1表示1/10每10个时间步出现一次脉冲；若等于1，表示持续出现
if ImpulseRestart_Times(1)>1
ImpulseRestart = ceil( ImpulseRestart_Times(1) );
elseif 0<ImpulseRestart_Times(1) && ImpulseRestart_Times(1)<1
ImpulseRestart = max(1, round( 1/ImpulseRestart_Times(1) ) );
else
error('restart is wrong');
end
ImpulseTimes = max(1, ceil( ImpulseRestart_Times(2) ) ); % 保证至少计算一个周期
N_max_time_seq = ImpulseRestart*ImpulseTimes ;
% % % % % % % % % % % % % % % % % % % % % % % % % % % % % %
% % disp(['Impulse type:',NormalizationType])
% IsEachAdjColNormalized = false; % % 文章中原始方法 未对每个网络标准化
% IsEachAdjColNormalized = true ; % % 对每个网络标准化 ，从概率上更好理解
InterLayerMappingType = 'FullMapping';
SetIsolatedNodeSelfLoop = true;
[A_nLxnL,N_node, L_net] = getMultiplexMatrixFromAdjSet_IN(AdjSet, weight_inter_net, IndividualNormalizationType, InterLayerMappingType, SetIsolatedNodeSelfLoop, DiffusionConstant) ;
A_nLxnL = getNormalizedMatrix_IN( A_nLxnL , NormalizationType, true );
A_nLxnL = diag(sum(A_nLxnL,2))-A_nLxnL ;
% if ~exist('tau_vec','var') || isempty(tau_vec)
% tau_vec = repmat(1/L_net,L_net,1);
% end
if ~issparse( A_nLxnL )
A_nLxnL = sparse( A_nLxnL );
end
% %
if isempty(delta_t_input)
delta_t = 0.005 ; % 0.01
else
delta_t = delta_t_input; %
end
%
% % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %
beta = 1 ;
% delta_t =0.005;
% ImpulseRestart = 1000;
Ut = [ 1; 0 ]*1;
P0 = reshape(P0,[],1) ;
B = [ P0, ~P0 ] ;
Bm = repmat(B, L_net, 1 );
BmUt = ( (1/delta_t)*Bm*Ut ) ;
Xt_seq = zeros(N_node*L_net,N_max_time_seq ) ;
X0 = ( ones( size(BmUt) ) *0 );
Xt1 = X0 ;
for T = 1: N_max_time_seq
if mod( T, ImpulseRestart ) ==1
Xt2 = Xt1 + ( - A_nLxnL*Xt1 - beta*( Xt1-X0 ) + ( BmUt ) ).*delta_t ; % impulsive signal
else
Xt2 = Xt1 + ( - A_nLxnL*Xt1 - beta*( Xt1-X0 ) ).*delta_t ;
end
Xt1 = Xt2 ;
Xt_seq(:, T ) = Xt2;
% check
if isdebug && ( any( isnan( Xt2(:) ) ) ) || any( isinf( Xt2(:) ) )
disp( [ 'There is nan or inf in Step: ', num2str( T ) ] );
disp( full( sum(A_nLxnL(:) ) ) );
disp( full( max(Xt2) ) );
disp( full( min(Xt2) ) );
if any( isinf( Xt2(:) ) ) || any( isnan( Xt2(:) ) )
find( Xt2( isinf( Xt2(:) ) ) )
find( Xt2( isnan( Xt2(:) ) ) )
error('There is nan or inf : Impulse dynamics. ' );
end
end
end
% % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %
i_seq = 1 ;
Xt_seq_final = Xt_seq(:, [N_max_time_seq-(i_seq)*ImpulseRestart+1: N_max_time_seq-(i_seq-1)*ImpulseRestart ] ); % exract final circle
Xt_seq_final_NxIRxL = zeros(N_node,ImpulseRestart, L_net ); % put into 3D array
for ii = 1: L_net
ids = (ii-1)*N_node + [1:N_node]; %
Xt_seq_final_NxIRxL(:,:,ii) = Xt_seq_final(ids,:);
end
%
% % EffSignatures = {'Xmax','Xmean','Vmt', 'cX5' } ;
[Res_allnets] = getTimeSeqFeaturesInOneCycle_IN( Xt_seq_final_NxIRxL ) ;
TableScores = table;
AdjSetfieldnameset = fieldnames( AdjSet);
Resfieldnameset_allnets = fieldnames( Res_allnets);
% RankGeoMean
for jj = 1: length( Resfieldnameset_allnets )
Scores = Res_allnets.(Resfieldnameset_allnets{jj})(:,1,:) ;
Ranks = getRankingOfScoreList_IN( Scores, 'descend' ) ;
% TableScores.([Resfieldnameset_allnets{jj},'_','rgm'] ) = -geomean( Ranks , 3 ) ;
TableScores.([Resfieldnameset_allnets{jj}] ) = -geomean( Ranks , 3 ) ;
end
% % % %
% % % if IndividualNetRes
% % % for ii_L_net = 1: L_net
% % % % ids = (ii_L_net-1)*N_node + [1:N_node]; %
% % % for jj = 1: length( Resfieldnameset_allnets )
% % % TableScores.([AdjSetfieldnameset{ii_L_net},'_',Resfieldnameset_allnets{jj}] ) = Res_allnets.(Resfieldnameset_allnets{jj})(:,1,ii_L_net) ;
% % % end
% % % end
% % % end
TableScores.Properties.VariableNames = strcat(['NIDM_',MDL,'_'] , TableScores.Properties.VariableNames );
if isempty(TableScores)
error('TableScores is empty')
end
end
% % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %
function [Res] = getTimeSeqFeaturesInOneCycle_IN( Xt_seq , minusFirst )
% % Xt_seq 每行对应一个节点的时序值；每一列对应一个时间点上所有节点的状态值; 第三维对应不同网络，每个网络的结果对应一层
if ~exist('minusFirst','var') || isempty( minusFirst )
% % minusFirst = true; % 是否计算增量
minusFirst = false; % 是否计算增量
end
% % minusFirst = 0;
if minusFirst
Xt_seq = Xt_seq-repmat(Xt_seq(:,1,:), 1, size(Xt_seq,2) );
end
[Xt_max, T_max ]= max(Xt_seq, [], 2);
Xt_mean = mean(Xt_seq, 2);
%
Res.Xmx = Xt_max; %峰值
Res.Xmn = Xt_mean;
Res.Vmt = Xt_max./(T_max +eps );
Res.Xmt = sum(Xt_seq(:,1:5,:),2 ) ; % default cX5
% % Res.Tinv = 1./(T_max +eps ); % 峰值 时间 的倒数
% % % Res.Xmin = Xt_min; %% Xt_min(1:5,:,:) %
% j_dX = 0;
% Res.(['X',num2str(j_dX +1)]) = Xt_seq(:,j_dX+1,:) ; j_dX = j_dX +1 ;
% Res.(['X',num2str(j_dX +1)]) = Xt_seq(:,j_dX+1,:) ; j_dX = j_dX +1 ;
% Res.(['X',num2str(j_dX +1)]) = Xt_seq(:,j_dX+1,:) ; j_dX = j_dX +1 ;
% Res.(['X',num2str(j_dX +1)]) = Xt_seq(:,j_dX+1,:) ; j_dX = j_dX +1 ;
% Res.(['X',num2str(j_dX +1)]) = Xt_seq(:,j_dX+1,:) ; j_dX = j_dX +1 ;
% Res.(['X',num2str(j_dX +1)]) = Xt_seq(:,j_dX+1,:) ; j_dX = j_dX +1 ;
% Res.(['X',num2str(j_dX +1)]) = Xt_seq(:,j_dX+1,:) ; j_dX = j_dX +1 ;
% % % % % % % %
% j_cumdX = 0;
% Res.(['cX',num2str(j_cumdX +1)]) = sum(Xt_seq(:,1:j_cumdX+1,:),2 ) ; j_cumdX = j_cumdX +1 ;
% Res.(['cX',num2str(j_cumdX +1)]) = sum(Xt_seq(:,1:j_cumdX+1,:),2 ) ; j_cumdX = j_cumdX +1 ;
% Res.(['cX',num2str(j_cumdX +1)]) = sum(Xt_seq(:,1:j_cumdX+1,:),2 ) ; j_cumdX = j_cumdX +1 ;
% Res.(['cX',num2str(j_cumdX +1)]) = sum(Xt_seq(:,1:j_cumdX+1,:),2 ) ; j_cumdX = j_cumdX +1 ;
% Res.(['cX',num2str(j_cumdX +1)]) = sum(Xt_seq(:,1:j_cumdX+1,:),2 ) ; j_cumdX = j_cumdX +1 ;
% Res.(['cX',num2str(j_cumdX +1)]) = sum(Xt_seq(:,1:j_cumdX+1,:),2 ) ; j_cumdX = j_cumdX +1 ;
% Res.(['cX',num2str(j_cumdX +1)]) = sum(Xt_seq(:,1:j_cumdX+1,:),2 ) ; j_cumdX = j_cumdX +1 ;
% % Res.base = rand( size(Xt_max) );
% Res.base = ones( size(Xt_max) ); % 性能评价函数将对其完全随机化 得到随机化结果
% % % % Res.base2 = ones( size(Xt_max) );
end
% % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %
function [Ranks, ord ]= getRankingOfScoreList_IN( ScoreList, sorttype, OP_IdenticalValues )
% by Xiang
% 2019-2
if ~exist('OP_IdenticalValues','var') || isempty(OP_IdenticalValues)
OP_IdenticalValues = 'MeanRank'; % % for elements with the same values
end
%
[~, ord ] = sort(ScoreList , 1, sorttype );
IsSparse_ScoreList = issparse( ScoreList );
if IsSparse_ScoreList
ScoreList = full( ScoreList );
end
IDlist = [1: size( ScoreList, 1 ) ]';
Ranks = zeros( size( ScoreList ) );
rank_t = zeros( size(IDlist) );
for d2=1:size( ScoreList ,2 )
for d3 = 1:size( ScoreList ,3)
for d4 = 1:size( ScoreList ,4)
score_t = ScoreList(:,d2,d3,d4);
rank_t(ord(:,d2,d3,d4)) = IDlist;
% Ranks( ord(:,d2,d3,d4), d2,d3,d4) = IDlist ;
%
if ~strcmpi(OP_IdenticalValues, 'None') % 对于相同元素值，采取操作
% % if strcmpi(OP_IdenticalValues, 'MeanRank') || strcmpi(OP_IdenticalValues, 'RandPermutation') % 对于相同元素值，采用平均 rank
[uniqueScores, ~,ic] = unique( score_t ) ;
if length( ic ) ~= length( uniqueScores )
for ii_uniqueScorese = 1: length( uniqueScores )
idx = ( ic== ii_uniqueScorese ) ;
n_thisscore = nnz( idx ) ;
if n_thisscore>1
if strcmpi(OP_IdenticalValues, 'MeanRank')
% Ranks( idx, d2,d3,d4 ) = mean(Ranks(idx, d2,d3,d4), 1); %对于相同分值的元素赋予相同的所有ranks的均值
rank_t( idx ) = mean(rank_t(idx), 1); %对于相同分值的元素赋予相同的所有ranks的均值
% % sum( labels_ord( idx ) )/nnz( idx )
elseif strcmpi(OP_IdenticalValues, 'RandPermutation')
ind = find(idx);
ind_randperm = ind( randperm( n_thisscore ) );
rank_t( ind ) = rank_t( ind_randperm );
else
error('There is no definition of OP_IdenticalValues');
end
end
% %sum(labels_ord)
end
end
end
Ranks( : , d2,d3,d4) = rank_t;
% %
end
end
end
if IsSparse_ScoreList %
Ranks = sparse( Ranks );
end
end
% % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %
function [A_nLxnL,N_node, L_net] = getMultiplexMatrixFromAdjSet_IN(AdjSet, pro_jump, NormalizationType, InterLayerMappingType, SetIsolatedNodeSelfLoop, DiffusionConstant)
if ~exist('NormalizationType','var')
NormalizationType = 'col';
elseif isempty(NormalizationType) || strcmpi(NormalizationType,'none')
NormalizationType = 'None';
end
%
if ~exist('InterLayerMappingType','var')
InterLayerMappingType ='FullMapping';
% % else
% % InterLayerMappingType ='PartialMapping'; %
end
%
if ~exist('SetIsolatedNodeSelfLoop','var')
SetIsolatedNodeSelfLoop = true;
end
%
if ~exist('DiffusionConstant','var') || isempty(DiffusionConstant)
DiffusionConstant = 1-pro_jump;
end
delta =pro_jump;
%
if isa(AdjSet,'struct') || isa(AdjSet,'table')
if isa(AdjSet,'struct')
fieldnameset = fieldnames( AdjSet);
elseif isa(AdjSet,'table')
fieldnameset = AdjSet.Properties.VariableNames;
end
L_net = length( fieldnameset );
N_node = length( AdjSet.(fieldnameset{1}));
NxL = N_node*L_net ;
%
if L_net==1; delta = 0; end %% no jumping for only single layer.
%
A_nLxnL = repmat( (delta/(L_net-1+eps)).*speye( N_node, N_node), L_net,L_net);
for ii_net = 1: L_net
idx = N_node*(ii_net-1)+[1: N_node ] ;
if strcmpi(NormalizationType,'None')
A_nLxnL(idx,idx)= (DiffusionConstant).*AdjSet.(fieldnameset{ii_net});
else
A_nLxnL(idx,idx)= (DiffusionConstant).*getNormalizedMatrix_IN( AdjSet.(fieldnameset{ii_net}) , NormalizationType, SetIsolatedNodeSelfLoop );
end
end
else
error(['AdjSet is wrong. It should be a cell matrix or struct.' ]);
end
end
% % % % % % % % % % % % % % % % % % % % % % % % % % % % % %
function WAdj = getNormalizedMatrix_IN(Adj, NormalizationType, SetIsolatedNodeSelfLoop ,IsMeanDegreeForExistNodeSet )
% % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %
% Adj adjecent matrix
% % NormalizationType:
% % 'probability normalization'
% % 'laplacian normalization'
% SetIsolatedNodeSelfLoop set isolated node
% >= Matlab 2016
% % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %
% if ~issparse(Adj)
% Adj = sparse( Adj );
% end
if ~exist('IsMeanDegreeForExistNodeSet','var') || isempty(IsMeanDegreeForExistNodeSet)
IsMeanDegreeForExistNodeSet = false;
end
if ischar(NormalizationType)
% NormalizationType = (NormalizationType);
switch lower( NormalizationType )
case lower( { 'column','col', ...
'ProbabilityNormalizationColumn','ProbabilityNormalizationCol',...
'ProbabilityColumnNormalization','ProbabilityColNormalization',...
'NormalizationColumn','NormalizationCol' , ...
'ColumnNormalization','ColNormalization' })
NormalizationName = 'ProbabilityNormalization' ; % 'Random Walk'
dim =1;
case lower({ 'row' ,'ProbabilityNormalizationRow' ,'NormalizationRow' ,'ProbabilityRowNormalization' ,'RowNormalization' })
NormalizationName = 'ProbabilityNormalization' ; % 'Random Walk'
dim =2;
case lower('LaplacianNormalization')
NormalizationName = NormalizationType;
case lower('LaplacianNormalizationMeanDegree')
NormalizationName = NormalizationType;
case lower('ColNorm2')
NormalizationName = NormalizationType;
case lower('RowNorm2')
NormalizationName = NormalizationType;
case lower({'none', 'None', 'NONE'})
% NormalizationName = 'None';
WAdj = Adj;
return;
otherwise
error(['There is no type of normalization: ',char( string(NormalizationType) )] );
end
elseif isnumeric( NormalizationType )
NormalizationName = ( 'ProbabilityNormalization' ) ; % 'Random Walk'
dim = NormalizationType;
elseif isempty( NormalizationType )
WAdj = Adj;
return;
else; error('There is no defintion of NormalizationType')
end
% NormalizationName = lower( NormalizationName );
% % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %
matlabveryear = textscan(version('-release'),'%d') ; matlabveryear=matlabveryear{1};
if matlabveryear>=2016
switch lower( NormalizationName )
case lower( 'ProbabilityNormalization' )
degrees = sum(Adj,dim);
if any( degrees~=1)
WAdj = Adj./ ( degrees+eps );
% % WAdj = Adj./ repmat( degrees +eps,[size(Adj,1),1]);
else
WAdj = Adj;
end
%
if SetIsolatedNodeSelfLoop && size(Adj,1)==size(Adj,2)
ii = find( ~degrees );
idx = sub2ind( size(Adj), ii,ii );
WAdj(idx) = 1; % set to be 1 for isolated nodes,
end
case lower( 'LaplacianNormalization')
deg_rowvec = ( sum(Adj,1) ).^0.5;
deg_colvec = ( sum(Adj,2) ).^0.5;
WAdj = (Adj./(deg_colvec+eps))./(deg_rowvec+eps) ;
%
if SetIsolatedNodeSelfLoop && size(Adj,1)==size(Adj,2)
ii = find( ~sum(Adj,2) ) ;
% size( WAdj )
% size( Adj )
WAdj( sub2ind( size(Adj), ii,ii ) ) = 1; % set to be 1 for isolated nodes,
end
case lower( 'LaplacianNormalizationMeanDegree')
% n_node = length( Adj );
k_col = sum( Adj, 2 );
k_row = sum( Adj, 1 );
if IsMeanDegreeForExistNodeSet
km1 = sum(k_col)./ (nnz(k_col)+eps);
km2 = sum(k_row)./ (nnz(k_row)+eps);
else
km1 = sum(k_col)./ length(k_col);
km2 = sum(k_row)./ length(k_row);
end
WAdj = Adj./( (km1.^0.5)*(km2.^0.5) +eps) ;
%
if SetIsolatedNodeSelfLoop && size(Adj,1)==size(Adj,2)
ii = find( ~sum(Adj,2) );
WAdj( sub2ind( size(Adj), ii,ii ) ) = 1; % set to be 1 for isolated nodes,
end
case lower( {'ColNorm2'} )
WAdj = Adj./ ( sqrt(sum( Adj.^2 ,1 )) +eps );
case lower( {'RowNorm2'} )
WAdj = Adj./ ( sqrt(sum( Adj.^2 ,2 )) +eps );
case lower( {'None','none'} )
WAdj = Adj; % 不做任何处理
otherwise
error(['NormalizationName is wrong: ',char(string(NormalizationName) ) ]);
end
% % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %
else
% error('errorerrorerrorerrorerrorerrorerrorerror');
switch lower( NormalizationName )
case lower( 'ProbabilityNormalization' )
degrees = sum(Adj,dim);
if any( degrees~=1)
% WAdj = Adj./ ( degrees+eps );
WAdj = getMatrixOperation(Adj, degrees, './') ;
% % WAdj = Adj./ repmat( degrees +eps,[size(Adj,1),1]);
else
WAdj = Adj;
end
%
if SetIsolatedNodeSelfLoop && size(Adj,1)==size(Adj,2)
ii = find( ~degrees );
idx = sub2ind( size(Adj), ii,ii );
WAdj(idx) = 1; % set to be 1 for isolated nodes,
end
case lower( 'LaplacianNormalization')
deg_rowvec = ( sum(Adj,1) ).^0.5;
deg_colvec = ( sum(Adj,2) ).^0.5;
% % WAdj = (Adj./(deg_colvec+eps))./(deg_rowvec+eps) ;
WAdj = getMatrixOperation(Adj, deg_colvec, './') ;
WAdj = getMatrixOperation(WAdj, deg_rowvec, './') ;
%
if SetIsolatedNodeSelfLoop && size(Adj,1)==size(Adj,2)
ii = find( ~sum(Adj,2) ) ;
% size( WAdj )
% size( Adj )
WAdj( sub2ind( size(Adj), ii,ii ) ) = 1; % set to be 1 for isolated nodes,
end
case lower( 'LaplacianNormalizationMeanDegree')
n_node = length( Adj );
km = sum( Adj(:) )./ n_node;
WAdj = Adj./( (km.^0.5)*(km.^0.5) +eps) ;
%
if SetIsolatedNodeSelfLoop && size(Adj,1)==size(Adj,2)
ii = find( ~sum(Adj,2) );
WAdj( sub2ind( size(Adj), ii,ii ) ) = 1; % set to be 1 for isolated nodes,
end
case lower( {'ColNorm2'} )
% % WAdj = Adj./ ( sqrt(sum( Adj.^2 ,1 )) +eps );
WAdj = getMatrixOperation(Adj, sqrt(sum( Adj.^2 ,1 )), './') ;
case lower( {'RowNorm2'} )
% % WAdj = Adj./ ( sqrt(sum( Adj.^2 ,2 )) +eps );
WAdj = getMatrixOperation(Adj, sqrt(sum( Adj.^2 ,2 )), './') ;
case lower( {'None','none'} )
WAdj = Adj; % 不做任何处理
otherwise
error(['NormalizationName is wrong: ',char(string(NormalizationName) ) ]);
end
end
end