有向图，无向图，网络图，加入权重。

import matplotlib.pyplot as plt
import networkx as nx
H = nx.path_graph(10) 
G.add_nodes_from(H)
nx.draw(G, with_labels=True)
plt.show()
G=nx.Graph()
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)
plt.show()
G = nx.Graph()   # or DiGraph, MultiGraph, MultiDiGraph, etc
nx.add_cycle(G, [0, 1, 2, 3])
nx.add_cycle(G, [10, 11, 12], weight=7)
nx.draw(G, with_labels=True)
plt.show()
import networkx as nx
import matplotlib.pyplot as plt 
G=nx.Graph()
G.add_node(1)
G.add_nodes_from([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,
        edge_color='b', 
        pos=nx.circular_layout(G), 
        node_color='r', 
        node_size=1000, 
        width=3,
       )
plt.show()
import random
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.show()
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)

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

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')

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

plt.axis('off')
plt.show()
from __future__ import division
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()
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()
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()