G_new=nx.Graph()# 依据图中边的数量,生成同样长度的随机权重值 weightList={}foriinrange(len(G.edges())+1):weightList[i]=np.random.rand()# 将生成的随机权重复制给G_new图 i=0foredgeinG.edges():i+=1G_new.add_edges_from([(edge[0],edge[1],{'weight':weightList[i]})])# 绘制G_...
找不到从属程序集 Microsoft.VC90.CRT,processorArchitecture="x86",publicKeyToken="1fc8b3b9a1e18e3b",type="win32",version="9.0.21022.8" windows服务器下利用IIS默认站点将服务器文件下载到本地电脑 在唯一密钥属性“value”设置为“index.asp”时,无法添加类型为“add”的重复集合项 Windows服务器上快速的创...
Random layout, where the nodes are uniformly at random placed in the unit square. This algorithm can be enabled with the keywords: 'Random', 'random', 'rand', or None Fruchterman-Reingold force-directed algorithm. In this algorithm, the nodes are represented by steel rings and the edges are...
self.W = Parameter(torch.rand(input_shape,output_shape)) #随机初始化权重 self.bias = Parameter(torch.rand(output_shape))#随机初始化偏置 def forward(self,Adj_matrix,input_feature): A = torch.from_numpy(Adj_matrix).type(torch.LongTensor) assert A.shape[0]== A.shape[1] #相等就正常执行...
def rand(infile): """ Performs robustness analysis based on random attack, on the network specified by infile. Returns a list with fraction of nodes removed, a list with the corresponding sizes of the largest component of the network, and the overall vulnerability of the network. """ g =...
_edges)) norm_conductance = subgraph_conductance - randgraph_conductance if not edge_cover: return norm_conductance return (norm_conductance, shared_edges) 浏览完整代码 来源:stats.py 项目:jim-pansn/sypy 示例25 networkxTestTopology(self, graph_links, graphnodes) # create a networkx graph ...
random.rand() if p < embedding_dist[i, j] * average_degree: G.add_edge(i, j) G.remove_nodes_from(nx.isolates(G)) print('num of nodes', G.number_of_nodes()) print('num of edges', G.number_of_edges()) G_deg = nx.degree_histogram(G) G_deg_sum = [a * b for a, b...
set_rule(rand_add) # Run simualation with tau=4 and Poisson density for motifs gmm.algorithms.simulate(model,4) # View results new_graph=model.get_base() print(nx.info(new_graph)) # Draw graphs old_pos=nx.spring_layout(old_graph) new_pos=nx.spring_layout(new_graph,iterations=2000) ...
degree(BG,x)==0 and layer[x]!=variables_index]; [fac.remove(x) for x in isolated_factors] G.remove_nodes_from(isolated_factors); while len(var)>0: if verbose==True: print '#var:',len(var),'#fac:', len(fac), '#nodes in depleted graph:', G.number_of_nodes(),'#original ...