import matplotlib.pyplot as plt # 将图形绘制成一个矩形布局 pos = nx.spring_layout(G, k=0.5, iterations=50) # 绘制图形的节点和边 nx.draw_networkx_nodes(G, pos) nx.draw_networkx_edges(G, pos) # 添加节点标签 node_labels = {node: str(node) for node in G.nodes()} nx.draw_networkx_labels(G, pos, node_labels) # 添加边权重标签 edge_labels = {(u, v): str(d['weight']) for u, v, d in G.edges(data=True)} nx.draw_networkx_edge_labels(G, pos, edge_labels) # 显示图形 plt.show() 修改代码，将图的标题改为“网络图”

可以在最后一行代码前添加以下代码来修改图的标题：

plt.title("网络图")

修改后的代码如下：

import matplotlib.pyplot as plt 
pos = nx.spring_layout(G, k=0.5, iterations=50) 
nx.draw_networkx_nodes(G, pos) 
nx.draw_networkx_edges(G, pos) 
node_labels = {node: str(node) for node in G.nodes()} 
nx.draw_networkx_labels(G, pos, node_labels) 
edge_labels = {(u, v): str(d['weight']) for u, v, d in G.edges(data=True)} 
nx.draw_networkx_edge_labels(G, pos, edge_labels) 
plt.title("网络图")
plt.show()

相关问题

import matplotlib.pyplot as plt# 将图形绘制成一个圆形布局pos = nx.circular_layout(G)# 绘制图形的节点和边nx.draw_networkx_nodes(G, pos)nx.draw_networkx_edges(G, pos)# 添加节点标签node_labels = {node: str(node) for node in G.nodes()}nx.draw_networkx_labels(G, pos, node_labels)# 添加边权重标签edge_labels = {(u, v): str(d['weight']) for u, v, d in G.edges(data=True)}nx.draw_networkx_edge_labels(G, pos, edge_labels)# 显示图形plt.show() 修改代码，将图形绘制成一个矩形布局

要将图形绘制成一个矩形布局，可以使用 `nx.spring_layout()` 或者 `nx.spectral_layout()` 方法来生成矩形布局。以下是使用 `nx.spring_layout()` 方法的示例代码：

import matplotlib.pyplot as plt
import networkx as nx

# 创建一个图形对象
G = nx.Graph()

# 添加节点和边
G.add_edges_from([(1, 2), (1, 3), (2, 3), (2, 4), (3, 4), (4, 5), (5, 6)])

# 生成矩形布局
pos = nx.spring_layout(G, k=0.5, iterations=50)

# 绘制图形的节点和边
nx.draw_networkx_nodes(G, pos)
nx.draw_networkx_edges(G, pos)

# 添加节点标签
node_labels = {node: str(node) for node in G.nodes()}
nx.draw_networkx_labels(G, pos, node_labels)

# 添加边权重标签
edge_labels = {(u, v): str(d['weight']) for u, v, d in G.edges(data=True)}
nx.draw_networkx_edge_labels(G, pos, edge_labels)

# 显示图形
plt.show()

其中，`nx.spring_layout()` 方法中的 `k` 参数控制节点之间的距离，`iterations` 参数控制迭代次数。你也可以尝试使用 `nx.spectral_layout()` 方法生成矩形布局。

修改代码，将图形绘制成一个矩形布局：import matplotlib.pyplot as plt# 将图形绘制成一个圆形布局pos = nx.circular_layout(G)# 绘制图形的节点和边nx.draw_networkx_nodes(G, pos)nx.draw_networkx_edges(G, pos)# 添加节点标签node_labels = {node: str(node) for node in G.nodes()}nx.draw_networkx_labels(G, pos, node_labels)# 添加边权重标签edge_labels = {(u, v): str(d['weight']) for u, v, d in G.edges(data=True)}nx.draw_networkx_edge_labels(G, pos, edge_labels)# 显示图形plt.show()

在这个示例代码中，我们可以使用 `nx.spring_layout()` 方法来生成矩形布局。以下是修改后的代码：

import matplotlib.pyplot as plt
import networkx as nx

# 创建一个图形对象
G = nx.Graph()

# 添加节点和边
G.add_edges_from([(1, 2), (1, 3), (2, 3), (2, 4), (3, 4), (4, 5), (5, 6)])

# 生成矩形布局
pos = nx.spring_layout(G, k=0.5, iterations=50)

# 绘制图形的节点和边
nx.draw_networkx_nodes(G, pos)
nx.draw_networkx_edges(G, pos)

# 添加节点标签
node_labels = {node: str(node) for node in G.nodes()}
nx.draw_networkx_labels(G, pos, node_labels)

# 添加边权重标签
edge_labels = {(u, v): str(d['weight']) for u, v, d in G.edges(data=True)}
nx.draw_networkx_edge_labels(G, pos, edge_labels)

# 显示图形
plt.show()

在这个修改后的代码中，我们只需要将 `pos` 变量的赋值改为 `nx.spring_layout(G, k=0.5, iterations=50)` 就可以生成矩形布局了。