property DiGraph.in_edges 图的一个线性视图,如g.in_edges或g.in_edges()。在“边”(Self,nbunch=none,Data=false,Default=none)中: 参数…
property MultiDiGraph.in_edges 图形的inmultiegeview,如g.in_edges或g.in_edges()。 在边缘(self,nbunch=none,data=false,keys=false,defa…
添加边属性使用 add_edge()、add_edges_from() 或下标表示法。特殊属性权重应该是数字，用于加权边的算法。处理有向图，使用 DiGraph 类提供特定于有向边的方法和属性，如 DiGraph.out_edges、DiGraph.in_degree 等。将有向图视为无向图，使用 Graph.to_undirected() 或其他转换。多图允许任意一对...
nx.draw_networkx(G,with_label=True) print("Total number of nodes: ",int(G.number_of_nodes())) print("Total number of edges: ",int(G.number_of_edges())) print("List of all nodes: ",list(G.nodes())) print("List of all edges: ",list(G.edges(data=True))) print("Degree for...
G.number_of_edges() # 统计边数 G.degree # 统计所有节点的度 G.add_node(1, age=10) # 添加节点,创建属性age G.add_node('2', age=15) # 节点命名很自由 G.nodes[1]; G.nodes['2'] # 查看节点属性 G.nodes['2']['weight'] = 4 # 新增节点属性 ...
edges(data=True) if d['weight'] >weight_threshold] esmall=[(u,v) for (u,v,d) in cox.edges(data=True) if d['weight'] <=weight_threshold] pos=nx.graphviz_layout(cox) # positions for all nodes nx.draw_networkx_nodes(cox,pos, node_color= [s*4500 for s in nx.eigenvector_...
添加节点和边到有向图中:G.add_edges_from([(1, 2), (2, 3), (3, 4)]) 使用networkx的in_degree()函数获取所有节点的入度:in_degrees = G.in_degree() 找到入度为0的节点,即根节点:root_nodes = [node for node, in_degree in in_degrees if in_degree == 0] 如果有多个根节点,可以选择...
num_nodes = len(G_3.nodes()) edges_in_cycle = {} for i in range(1,num_nodes+1): edges_in_cycle[i] = nx.find_cycle(G_3,i)` edges_in_cycle >>> {1: [(2, 4), (4, 3), (3, 2)], 2: [(2, 1), (1, 4), (4, 2)], 3: [(2, 1), (1, 4), (4, 2)]...
# print("non_edges: ", G.non_edges) # 返回图中不存在的边 print("all_neighbors: ") # 返回图中节点的所有邻居 a = nx.all_neighbors(G, "f") for i, node in enumerate(a): print(i, node) print("non_neighbors: ") # 返回图中没有邻居的节点 ...
estrong = [(u,v) for (u,v,d) in g.edges(data = True) if d["weigth"] >THESHOLD] 2. 基本分析 边数与点数 print g.number_of_nodes() print g.number_of_edges() 度 分析某个顶点的度(无向图) g.degree("nodename") 分析某个顶点的入度\出度(有向图) ...