python绘制社会关系网络图_Python networkx 网络图绘制

简单演示

import networkx as nx

import matplotlib.pyplot as plt

# 定义空图

g = nx.Graph()

# 增加节点

g.add_node(1)

g.add_node('A')

g.add_nodes_from([2, 3])

g.add_edge(2, 3)

g.add_edges_from([(1, 2), (1, 3)])

nx.draw(g, with_labels=True)

plt.show()

一次增加多个点

import networkx as nx

import matplotlib.pyplot as plt

# 定义空图

g = nx.Graph()

h = nx.path_graph(10)

g.add_nodes_from(h)

nx.draw(g, with_labels=True)

plt.show()

有颜色的图

import networkx as nx

import matplotlib.pyplot as plt

# 定义空图

g = nx.Graph()

# 导入所有边，每条边分别用tuple表示

g.add_edges_from([(1, 2), (1, 3), (2, 4), (2, 5), (3, 6), (4, 8), (5, 8), (3, 7)])

nx.draw(g,

with_labels=True,

edge_color='b',

node_color='g',

node_size=1000)

# 如果你想保存图片，去除这句的注释

# plt.savefig('./generated_image.png')

plt.show()

环

import networkx as nx

import matplotlib.pyplot as plt

# 定义空图

g = nx.Graph()

g.add_cycle([1, 2, 3, 4, 5])

nx.draw(g, with_labels=True)

plt.show()

五角星

import networkx as nx

import matplotlib.pyplot as plt

G = nx.Graph()

G.add_nodes_from([1, 2, 3, 4, 5])

for i in range(5):

for j in range(i):

if (abs(i - j) not in (1, 4)):

G.add_edge(i + 1, j + 1)

nx.draw(G,

with_labels=True, # 这个选项让节点有名称

edge_color='b', # b stands for blue!

pos=nx.circular_layout(G), # 这个是选项选择点的排列方式，具体可以用 help(nx.drawing.layout) 查看

# 主要有spring_layout (default), random_layout, circle_layout, shell_layout

# 这里是环形排布，还有随机排列等其他方式

node_color='r', # r = red

node_size=1000, # 节点大小

width=3, # 边的宽度

)

plt.show()

随机绘图

import random

import networkx as nx

import matplotlib.pyplot as plt

G = nx.gnp_random_graph(10, 0.3)

for u, v, d in G.edges(data=True):

d['weight'] = random.random()

edges, weights = zip(*nx.get_edge_attributes(G, 'weight').items())

pos = nx.spring_layout(G)

nx.draw(G, pos, node_color='b', edgelist=edges, edge_color=weights, width=10.0, edge_cmap=plt.cm.Blues)

# plt.savefig('edges.png')

plt.show()

有权重的图

import matplotlib.pyplot as plt

import networkx as nx

G = nx.Graph()

G.add_edge('a', 'b', weight=0.6)

G.add_edge('a', 'c', weight=0.2)

G.add_edge('c', 'd', weight=0.1)

G.add_edge('c', 'e', weight=0.7)

G.add_edge('c', 'f', weight=0.9)

G.add_edge('a', 'd', weight=0.3)

elarge = [(u, v) for (u, v, d) in G.edges(data=True) if d['weight'] > 0.5]

esmall = [(u, v) for (u, v, d) in G.edges(data=True) if d['weight'] <= 0.5]

pos = nx.spring_layout(G) # positions for all nodes

# nodes

nx.draw_networkx_nodes(G, pos, node_size=700)

# edges

nx.draw_networkx_edges(G, pos, edgelist=elarge,

width=6)

nx.draw_networkx_edges(G, pos, edgelist=esmall,

width=6, alpha=0.5, edge_color='b', style='dashed')

# labels

nx.draw_networkx_labels(G, pos, font_size=20, font_family='sans-serif')

plt.axis('off')

plt.show()

有向图

import matplotlib.pyplot as plt

import networkx as nx

G = nx.generators.directed.random_k_out_graph(10, 3, 0.5)

pos = nx.layout.spring_layout(G)

node_sizes = [3 + 10 * i for i in range(len(G))]

M = G.number_of_edges()

edge_colors = range(2, M + 2)

edge_alphas = [(5 + i) / (M + 4) for i in range(M)]

nodes = nx.draw_networkx_nodes(G, pos, node_size=node_sizes, node_color='blue')

edges = nx.draw_networkx_edges(G, pos, node_size=node_sizes, arrowstyle='->',

arrowsize=10, edge_color=edge_colors,

edge_cmap=plt.cm.Blues, width=2)

# set alpha value for each edge

for i in range(M):

edges[i].set_alpha(edge_alphas[i])

ax = plt.gca()

ax.set_axis_off()

plt.show()

颜色渐变的节点

import matplotlib.pyplot as plt

import networkx as nx

G = nx.cycle_graph(24)

pos = nx.spring_layout(G, iterations=200)

nx.draw(G, pos, node_color=range(24), node_size=800, cmap=plt.cm.Blues)

plt.show()

颜色渐变的边

import matplotlib.pyplot as plt

import networkx as nx

G = nx.star_graph(20)

pos = nx.spring_layout(G)

colors = range(20)

nx.draw(G, pos, node_color='#A0CBE2', edge_color=colors,

width=4, edge_cmap=plt.cm.Blues, with_labels=False)

plt.show()

多层感知器

import matplotlib.pyplot as plt

import networkx as nx

left, right, bottom, top, layer_sizes = .1, .9, .1, .9, [4, 7, 7, 2]

# 网络离上下左右的距离

# layter_sizes可以自己调整

import random

G = nx.Graph()

v_spacing = (top - bottom) / float(max(layer_sizes))

h_spacing = (right - left) / float(len(layer_sizes) - 1)

node_count = 0

for i, v in enumerate(layer_sizes):

layer_top = v_spacing * (v - 1) / 2. + (top + bottom) / 2.

for j in range(v):

G.add_node(node_count, pos=(left + i * h_spacing, layer_top - j * v_spacing))

node_count += 1

# 这上面的数字调整我想了好半天，汗

for x, (left_nodes, right_nodes) in enumerate(zip(layer_sizes[:-1], layer_sizes[1:])):

for i in range(left_nodes):

for j in range(right_nodes):

G.add_edge(i + sum(layer_sizes[:x]), j + sum(layer_sizes[:x + 1]))

# 慢慢研究吧

pos = nx.get_node_attributes(G, 'pos')

# 把每个节点中的位置pos信息导出来

nx.draw(G, pos,

node_color=range(node_count),

with_labels=True,

node_size=200,

edge_color=[random.random() for i in range(len(G.edges))],

width=3,

cmap=plt.cm.Dark2, # matplotlib的调色板，可以搜搜，很多颜色呢

edge_cmap=plt.cm.Blues

)

plt.show()

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