我尝试使用随机游走方法查找顶点相似度，在这项工作中，使用了一个过渡矩阵。每当我尝试运行使用python实现的代码时，都会出现此错误。我也读过类似的问题，但没有具体答案。您能帮我解决这个问题吗，真的很感谢您的帮助。
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RuntimeError Traceback (most recent call last)
<ipython-input-259-2639b08a8eb7> in <module>()
45
46
---> 47 tuple_steps_prob,b=similarities(training_graph,test_edge_list)
48 print(tuple_steps_prob)
49 # pre_list_=Precision(tuple_steps_prob, test_edge_list,test_num,b)
<ipython-input-237-e0348fd15773> in similarities(graph, test_edge_list)
16 prob_vec[0][k] = 1
17 #print(prob_vec)
---> 18 extracted,prob,y=RandomWalk(graph,nodes,adj,prob_vec)
19
20 j=0
<ipython-input-236-6b0298295e01> in RandomWalk(G, nodes, adj, prob_vec)
31 beta_=0.1
32
---> 33 TM = Transition_Matrix(adj,beta_)
34
35 extracted1=[]
~\Desktop\RW\RW\Transition_Probability_Matrix.py in Transition_Matrix(adj, beta_)
18
19 Iden=np.identity(len(TM))
---> 20
21
22 Transition=beta_/(1+beta_) * Iden + 1/(1+beta_) * TM
~\Anaconda3\lib\site-packages\scipy\sparse\linalg\matfuncs.py in inv(A)
72 """
73 I = speye(A.shape[0], A.shape[1], dtype=A.dtype, format=A.format)
---> 74 Ainv = spsolve(A, I)
75 return Ainv
76
~\Anaconda3\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py in spsolve(A, b, permc_spec, use_umfpack)
196 else:
197 # b is sparse
--> 198 Afactsolve = factorized(A)
199
200 if not isspmatrix_csc(b):
~\Anaconda3\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py in factorized(A)
438 return solve
439 else:
--> 440 return splu(A).solve
441
442
~\Anaconda3\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py in splu(A, permc_spec, diag_pivot_thresh, relax, panel_size, options)
307 _options.update(options)
308 return _superlu.gstrf(N, A.nnz, A.data, A.indices, A.indptr,
--> 309 ilu=False, options=_options)
310
311
RuntimeError: Factor is exactly singular