✂️梗概

网络关系生成

步骤1:在项目文件中导入networkx和matplotlib.pyplot。

import networkx as nx
import matplotlib.pyplot as plt

步骤2:使用 networkx 生成图表。

步骤3:现在使用networkx.drawing的draw()函数来绘制图形。

步骤4:使用matplotlib.pyplot的savefig(“filename.png”)函数将绘制的图形保存在filename.png文件中。

import networkx as nx
import matplotlib.pyplot as plt

g = nx.Graph()

g.add_edge(1, 2)
g.add_edge(2, 3)
g.add_edge(3, 4)
g.add_edge(1, 4)
g.add_edge(1, 5)

nx.draw(g)
plt.savefig("filename.png")

输出:

Untitled

data:image/png;base64,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

要在节点中添加编号,请在 draw() 函数中添加一个参数 with_labels=True 。

import networkx as nx
import matplotlib.pyplot as plt

g = nx.Graph()

g.add_edge(1, 2)
g.add_edge(2, 3)
g.add_edge(3, 4)
g.add_edge(1, 4)
g.add_edge(1, 5)

nx.draw(g, with_labels = True)
plt.savefig("filename.png")

输出:

Untitled

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

可以使用 networkx 绘图和 matplotlib 完成不同的图形类型和绘图。

import networkx as nx
import matplotlib.pyplot as plt

g = nx.Graph()

g.add_edge(1, 2)
g.add_edge(2, 3)
g.add_edge(3, 4)
g.add_edge(1, 4)
g.add_edge(1, 5)
g.add_edge(5, 6)
g.add_edge(5, 7)
g.add_edge(4, 8)
g.add_edge(3, 8)

nx.draw_circular(g, with_labels = True)
plt.savefig("filename1.png")

plt.clf()

nx.draw_planar(g, with_labels = True)
plt.savefig("filename2.png")
plt.clf()

nx.draw_random(g, with_labels = True)
plt.savefig("filename3.png")
plt.clf()

nx.draw_spectral(g, with_labels = True)
plt.savefig("filename4.png")
plt.clf()

nx.draw_spring(g, with_labels = True)
plt.savefig("filename5.png")
plt.clf()

nx.draw_shell(g, with_labels = True)
plt.savefig("filename6.png")
plt.clf()

输出:

圆形布局: