[python] NetworkX实例

NetworkX实例

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NetworkX 2.4版本的通用示例性示例。本教程介绍了约定和基本的图形操作。具体章节内容如下：

1. 基础Basic
2. 绘图Drawing
3. 图标Graph

本文参考：

https://networkx.github.io/documentation/stable/auto_examples/index.html

1. 基础Basic

• 读写图 Read and write graphs
• 属性 Properties

## 读写图 Read and write graphs

# Author: Aric Hagberg ([email protected])

#    Copyright (C) 2004-2019 by
#    Aric Hagberg 
#    Dan Schult 
#    Pieter Swart 
#    All rights reserved.
#    BSD license.
import sys
import matplotlib.pyplot as plt
import networkx as nx

# 生成网格
G = nx.grid_2d_graph(5, 5)  # 5x5 grid

# print the adjacency list
# 打印网络
for line in nx.generate_adjlist(G):
    print(line)
# write edgelist to grid.edgelist
# 写数据
#nx.write_edgelist(G, path="grid.edgelist", delimiter=":")
# read edgelist from grid.edgelist
# 读数据
#H = nx.read_edgelist(path="grid.edgelist", delimiter=":")

nx.draw(G)

plt.show()

(0, 0) (1, 0) (0, 1)
(0, 1) (1, 1) (0, 2)
(0, 2) (1, 2) (0, 3)
(0, 3) (1, 3) (0, 4)
(0, 4) (1, 4)
(1, 0) (2, 0) (1, 1)
(1, 1) (2, 1) (1, 2)
(1, 2) (2, 2) (1, 3)
(1, 3) (2, 3) (1, 4)
(1, 4) (2, 4)
(2, 0) (3, 0) (2, 1)
(2, 1) (3, 1) (2, 2)
(2, 2) (3, 2) (2, 3)
(2, 3) (3, 3) (2, 4)
(2, 4) (3, 4)
(3, 0) (4, 0) (3, 1)
(3, 1) (4, 1) (3, 2)
(3, 2) (4, 2) (3, 3)
(3, 3) (4, 3) (3, 4)
(3, 4) (4, 4)
(4, 0) (4, 1)
(4, 1) (4, 2)
(4, 2) (4, 3)
(4, 3) (4, 4)
(4, 4)

## 属性 Properties

#    Copyright (C) 2004-2019 by
#    Aric Hagberg 
#    Dan Schult 
#    Pieter Swart 
#    All rights reserved.
#    BSD license.

import matplotlib.pyplot as plt
from networkx import nx

G = nx.lollipop_graph(4, 6)

pathlengths = []

print("source vertex {target:length, }")
for v in G.nodes():
    spl = dict(nx.single_source_shortest_path_length(G, v))
    print('{} {} '.format(v, spl))
    for p in spl:
        pathlengths.append(spl[p])

print('')
print("average shortest path length %s" % (sum(pathlengths) / len(pathlengths)))

# histogram of path lengths
dist = {}
for p in pathlengths:
    if p in dist:
        dist[p] += 1
    else:
        dist[p] = 1

print('')
print("length #paths")
verts = dist.keys()
for d in sorted(verts):
    print('%s %d' % (d, dist[d]))

print("radius: %d" % nx.radius(G))
print("diameter: %d" % nx.diameter(G))
print("eccentricity: %s" % nx.eccentricity(G))
print("center: %s" % nx.center(G))
print("periphery: %s" % nx.periphery(G))
print("density: %s" % nx.density(G))

nx.draw(G, with_labels=True)
plt.show()

source vertex {target:length, }
0 {0: 0, 1: 1, 2: 1, 3: 1, 4: 2, 5: 3, 6: 4, 7: 5, 8: 6, 9: 7} 
1 {1: 0, 0: 1, 2: 1, 3: 1, 4: 2, 5: 3, 6: 4, 7: 5, 8: 6, 9: 7} 
2 {2: 0, 0: 1, 1: 1, 3: 1, 4: 2, 5: 3, 6: 4, 7: 5, 8: 6, 9: 7} 
3 {3: 0, 0: 1, 1: 1, 2: 1, 4: 1, 5: 2, 6: 3, 7: 4, 8: 5, 9: 6} 
4 {4: 0, 5: 1, 3: 1, 6: 2, 0: 2, 1: 2, 2: 2, 7: 3, 8: 4, 9: 5} 
5 {5: 0, 4: 1, 6: 1, 3: 2, 7: 2, 0: 3, 1: 3, 2: 3, 8: 3, 9: 4} 
6 {6: 0, 5: 1, 7: 1, 4: 2, 8: 2, 3: 3, 9: 3, 0: 4, 1: 4, 2: 4} 
7 {7: 0, 6: 1, 8: 1, 5: 2, 9: 2, 4: 3, 3: 4, 0: 5, 1: 5, 2: 5} 
8 {8: 0, 7: 1, 9: 1, 6: 2, 5: 3, 4: 4, 3: 5, 0: 6, 1: 6, 2: 6} 
9 {9: 0, 8: 1, 7: 2, 6: 3, 5: 4, 4: 5, 3: 6, 0: 7, 1: 7, 2: 7} 

average shortest path length 2.86

length #paths
0 10
1 24
2 16
3 14
4 12
5 10
6 8
7 6
radius: 4
diameter: 7
eccentricity: {0: 7, 1: 7, 2: 7, 3: 6, 4: 5, 5: 4, 6: 4, 7: 5, 8: 6, 9: 7}
center: [5, 6]
periphery: [0, 1, 2, 9]
density: 0.26666666666666666

2. 绘图Drawing

• 简单路径Simple Path
• 节点颜色Node Colormap
• 边的颜色 Edge Colormap
• 带颜色的房子 House With Colors
• 环形树Circular Tree
• 等级排列Degree Rank
• 谱嵌入Spectral Embedding
• 四宫格Four Grids
• 自我中心网络Ego Graph
• 度直方图Degree histogram
• 随机几何图形Random Geometric Graph
• 加权图Weighted Graph
• 有向图Directed Graph
• 标签和颜色Labels And Colors
• 最大连通分支Giant Component
• 地图集Atlas

## 简单路径Simple Path

import matplotlib.pyplot as plt
import networkx as nx

G = nx.path_graph(8)
nx.draw(G)
plt.show()

## 节点颜色Node Colormap

# Author: Aric Hagberg ([email protected])

import matplotlib.pyplot as plt
import networkx as nx

G = nx.cycle_graph(24)
# 设置排列位置，iterations迭代次数
pos = nx.spring_layout(G, iterations=200)
# node_color节点颜色
nx.draw(G, pos, node_color=range(24), node_size=800, cmap=plt.cm.Blues)
plt.show()

## 边的颜色 Edge Colormap

# Author: Aric Hagberg ([email protected])

import matplotlib.pyplot as plt
import networkx as nx

G = nx.star_graph(20)
pos = nx.spring_layout(G)
colors = range(20)
# edge_color边的颜色
nx.draw(G, pos, node_color='#A0CBE2', edge_color=colors,
        width=4, edge_cmap=plt.cm.Blues, with_labels=False)
plt.show()

## 带颜色的房子 House With Colors

# Author: Aric Hagberg ([email protected])
import matplotlib.pyplot as plt
import networkx as nx

G = nx.house_graph()
# explicitly set positions
pos = {0: (0, 0),
       1: (1, 0),
       2: (0, 1),
       3: (1, 1),
       4: (0.5, 2.0)}

# 画节点
nx.draw_networkx_nodes(G, pos, node_size=2000, nodelist=[4])
nx.draw_networkx_nodes(G, pos, node_size=3000, nodelist=[0, 1, 2, 3], node_color='b')
# 画线条
nx.draw_networkx_edges(G, pos, alpha=0.5, width=6)
plt.axis('off')
plt.show()

## 环形树Circular Tree
# 管理员权限下 pip install pydot

import matplotlib.pyplot as plt
import networkx as nx
import pydot
from networkx.drawing.nx_pydot import graphviz_layout

G = nx.balanced_tree(3, 5)
# 设置环形布置
pos = graphviz_layout(G, prog='twopi')
plt.figure(figsize=(8, 8))
nx.draw(G, pos, node_size=20, alpha=0.5, node_color="blue", with_labels=False)
plt.axis('equal')
plt.show()

## 等级排列Degree Rank

# Author: Aric Hagberg 
import networkx as nx
import matplotlib.pyplot as plt

G = nx.gnp_random_graph(100, 0.02)

degree_sequence = sorted([d for n, d in G.degree()], reverse=True)
# print "Degree sequence", degree_sequence
dmax = max(degree_sequence)

plt.loglog(degree_sequence, 'b-', marker='o')
plt.title("Degree rank plot")
plt.ylabel("degree")
plt.xlabel("rank")

# draw graph in inset
plt.axes([0.45, 0.45, 0.45, 0.45])
Gcc = G.subgraph(sorted(nx.connected_components(G), key=len, reverse=True)[0])
pos = nx.spring_layout(Gcc)
plt.axis('off')
nx.draw_networkx_nodes(Gcc, pos, node_size=20)
nx.draw_networkx_edges(Gcc, pos, alpha=0.4)

plt.show()

## 谱嵌入Spectral Embedding

import matplotlib.pyplot as plt
import networkx as nx


options = {
    'node_color': 'C0',
    'node_size': 100,
}

G = nx.grid_2d_graph(6, 6)
plt.subplot(332)
nx.draw_spectral(G, **options)

G.remove_edge((2, 2), (2, 3))
plt.subplot(334)
nx.draw_spectral(G, **options)

G.remove_edge((3, 2), (3, 3))
plt.subplot(335)
nx.draw_spectral(G, **options)

G.remove_edge((2, 2), (3, 2))
plt.subplot(336)
nx.draw_spectral(G, **options)

G.remove_edge((2, 3), (3, 3))
plt.subplot(337)
nx.draw_spectral(G, **options)

G.remove_edge((1, 2), (1, 3))
plt.subplot(338)
nx.draw_spectral(G, **options)

G.remove_edge((4, 2), (4, 3))
plt.subplot(339)
nx.draw_spectral(G, **options)

plt.show()

## 四宫格Four Grids

# Author: Aric Hagberg ([email protected])

#    Copyright (C) 2004-2019
#    Aric Hagberg 
#    Dan Schult 
#    Pieter Swart 
#    All rights reserved.
#    BSD license.

import matplotlib.pyplot as plt
import networkx as nx

# 生成四宫格点
G = nx.grid_2d_graph(4, 4)  # 4x4 grid

# 点排列
pos = nx.spring_layout(G, iterations=100)

plt.subplot(221)
nx.draw(G, pos, font_size=8)

plt.subplot(222)
# node_color节点的颜色
nx.draw(G, pos, node_color='k', node_size=0, with_labels=False)

plt.subplot(223)
nx.draw(G, pos, node_color='g', node_size=250, with_labels=False, width=6)

plt.subplot(224)
# 设置为有向图
H = G.to_directed()
nx.draw(H, pos, node_color='b', node_size=20, with_labels=False)

plt.show()

## 自我中心网络Ego Graph

# Author:  Drew Conway ([email protected])

from operator import itemgetter

import matplotlib.pyplot as plt
import networkx as nx

if __name__ == '__main__':
    # Create a BA model graph
    n = 1000
    m = 2
    G = nx.generators.barabasi_albert_graph(n, m)
    # find node with largest degree
    node_and_degree = G.degree()
    (largest_hub, degree) = sorted(node_and_degree, key=itemgetter(1))[-1]
    # Create ego graph of main hub
    hub_ego = nx.ego_graph(G, largest_hub)
    # Draw graph
    pos = nx.spring_layout(hub_ego)
    nx.draw(hub_ego, pos, node_color='b', node_size=50, with_labels=False)
    # Draw ego as large and red
    nx.draw_networkx_nodes(hub_ego, pos, nodelist=[largest_hub], node_size=300, node_color='r')
    plt.show()

## 度直方图Degree histogram

import collections
import matplotlib.pyplot as plt
import networkx as nx

G = nx.gnp_random_graph(100, 0.02)

degree_sequence = sorted([d for n, d in G.degree()], reverse=True)  # degree sequence
# print "Degree sequence", degree_sequence
degreeCount = collections.Counter(degree_sequence)
deg, cnt = zip(*degreeCount.items())

fig, ax = plt.subplots()
plt.bar(deg, cnt, width=0.80, color='b')

plt.title("Degree Histogram")
plt.ylabel("Count")
plt.xlabel("Degree")
ax.set_xticks([d + 0.4 for d in deg])
ax.set_xticklabels(deg)

# draw graph in inset
plt.axes([0.4, 0.4, 0.5, 0.5])
Gcc = G.subgraph(sorted(nx.connected_components(G), key=len, reverse=True)[0])
pos = nx.spring_layout(G)
plt.axis('off')
nx.draw_networkx_nodes(G, pos, node_size=20)
nx.draw_networkx_edges(G, pos, alpha=0.4)

plt.show()

## 随机几何图形Random Geometric Graph

import matplotlib.pyplot as plt
import networkx as nx

G = nx.random_geometric_graph(200, 0.125)
# position is stored as node attribute data for random_geometric_graph
pos = nx.get_node_attributes(G, 'pos')

# find node near center (0.5,0.5)
dmin = 1
ncenter = 0
for n in pos:
    x, y = pos[n]
    d = (x - 0.5)**2 + (y - 0.5)**2
    if d < dmin:
        ncenter = n
        dmin = d

# color by path length from node near center
p = dict(nx.single_source_shortest_path_length(G, ncenter))

plt.figure(figsize=(8, 8))
nx.draw_networkx_edges(G, pos, nodelist=[ncenter], alpha=0.4)
nx.draw_networkx_nodes(G, pos, nodelist=list(p.keys()),
                       node_size=80,
                       node_color=list(p.values()),
                       cmap=plt.cm.Reds_r)

plt.xlim(-0.05, 1.05)
plt.ylim(-0.05, 1.05)
plt.axis('off')
plt.show()

## 加权图Weighted Graph

# Author: Aric Hagberg ([email protected])
import matplotlib.pyplot as plt
import networkx as nx

G = nx.Graph()

# 确定边
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)

# 长边 权重大于0.5
elarge = [(u, v) for (u, v, d) in G.edges(data=True) if d['weight'] > 0.5]
# 短边 权重小于0.5
esmall = [(u, v) for (u, v, d) in G.edges(data=True) if d['weight'] <= 0.5]

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

# nodes
# 画节点
nx.draw_networkx_nodes(G, pos, node_size=700)

# edges
# 画边
nx.draw_networkx_edges(G, pos, edgelist=elarge,width=6)
# style边的样式
nx.draw_networkx_edges(G, pos, edgelist=esmall,width=6, alpha=0.5, edge_color='b', style='dashed')

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

plt.axis('off')
plt.show()

## 有向图Directed Graph

# Author: Rodrigo Dorantes-Gilardi ([email protected])

import matplotlib as mpl
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])

pc = mpl.collections.PatchCollection(edges, cmap=plt.cm.Blues)
pc.set_array(edge_colors)
plt.colorbar(pc)

ax = plt.gca()
ax.set_axis_off()
plt.show()

## 标签和颜色Labels And Colors

# Author: Aric Hagberg ([email protected])
import matplotlib.pyplot as plt
import networkx as nx

# 生成立体图
G = nx.cubical_graph()
# 确定位置
pos = nx.spring_layout(G)  # positions for all nodes

# nodes
# 画节点
nx.draw_networkx_nodes(G, pos,
                       nodelist=[0, 1, 2, 3],
                       node_color='r',
                       node_size=500,
                       alpha=0.8)
nx.draw_networkx_nodes(G, pos,
                       nodelist=[4, 5, 6, 7],
                       node_color='b',
                       node_size=500,
                       alpha=0.8)

# edges
nx.draw_networkx_edges(G, pos, width=1.0, alpha=0.5)
nx.draw_networkx_edges(G, pos,
                       edgelist=[(0, 1), (1, 2), (2, 3), (3, 0)],
                       width=8, alpha=0.5, edge_color='r')
nx.draw_networkx_edges(G, pos,
                       edgelist=[(4, 5), (5, 6), (6, 7), (7, 4)],
                       width=8, alpha=0.5, edge_color='b')


# some math labels
labels = {}
labels[0] = r'$a$'
labels[1] = r'$b$'
labels[2] = r'$c$'
labels[3] = r'$d$'
labels[4] = r'$\alpha$'
labels[5] = r'$\beta$'
labels[6] = r'$\gamma$'
labels[7] = r'$\delta$'
# 填写标签
nx.draw_networkx_labels(G, pos, labels, font_size=16)

plt.axis('off')
plt.show()

## 最大连通分支Giant Component 

#    Copyright (C) 2006-2019
#    Aric Hagberg 
#    Dan Schult 
#    Pieter Swart 
#    All rights reserved.
#    BSD license.

import math

import matplotlib.pyplot as plt
import networkx as nx

try:
    import pygraphviz
    from networkx.drawing.nx_agraph import graphviz_layout
    layout = graphviz_layout
except ImportError:
    try:
        import pydot
        from networkx.drawing.nx_pydot import graphviz_layout
        layout = graphviz_layout
    except ImportError:
        print("PyGraphviz and pydot not found;\n"
              "drawing with spring layout;\n"
              "will be slow.")
        layout = nx.spring_layout


n = 150  # 150 nodes
# p value at which giant component (of size log(n) nodes) is expected
p_giant = 1.0 / (n - 1)
# p value at which graph is expected to become completely connected
p_conn = math.log(n) / float(n)

# the following range of p values should be close to the threshold
pvals = [0.003, 0.006, 0.008, 0.015]

region = 220  # for pylab 2x2 subplot layout
plt.subplots_adjust(left=0, right=1, bottom=0, top=0.95, wspace=0.01, hspace=0.01)
for p in pvals:
    G = nx.binomial_graph(n, p)
    pos = layout(G)
    region += 1
    plt.subplot(region)
    plt.title("p = %6.3f" % (p))
    nx.draw(G, pos,
            with_labels=False,
            node_size=10
           )
    # identify largest connected component
    Gcc = sorted(nx.connected_components(G), key=len, reverse=True)
    G0 = G.subgraph(Gcc[0])
    nx.draw_networkx_edges(G0, pos,
                           with_labels=False,
                           edge_color='r',
                           width=6.0
                          )
    # show other connected components
    for Gi in Gcc[1:]:
        if len(Gi) > 1:
            nx.draw_networkx_edges(G.subgraph(Gi), pos,
                                   with_labels=False,
                                   edge_color='r',
                                   alpha=0.3,
                                   width=5.0
                                  )
plt.show()

## 地图集Atlas

# Author: Aric Hagberg ([email protected])

#    Copyright (C) 2004-2019 by
#    Aric Hagberg 
#    Dan Schult 
#    Pieter Swart 
#    All rights reserved.
#    BSD license.

import random

try:
    import pygraphviz
    from networkx.drawing.nx_agraph import graphviz_layout
except ImportError:
    try:
        import pydot
        from networkx.drawing.nx_pydot import graphviz_layout
    except ImportError:
        raise ImportError("This example needs Graphviz and either "
                          "PyGraphviz or pydot.")

import matplotlib.pyplot as plt

import networkx as nx
from networkx.algorithms.isomorphism.isomorph import graph_could_be_isomorphic as isomorphic
from networkx.generators.atlas import graph_atlas_g


def atlas6():
    """ Return the atlas of all connected graphs of 6 nodes or less.
        Attempt to check for isomorphisms and remove.
    """

    Atlas = graph_atlas_g()[0:208]  # 208
    # remove isolated nodes, only connected graphs are left
    U = nx.Graph()  # graph for union of all graphs in atlas
    for G in Atlas:
        zerodegree = [n for n in G if G.degree(n) == 0]
        for n in zerodegree:
            G.remove_node(n)
        U = nx.disjoint_union(U, G)

    # iterator of graphs of all connected components
    C = (U.subgraph(c) for c in nx.connected_components(U))

    UU = nx.Graph()
    # do quick isomorphic-like check, not a true isomorphism checker
    nlist = []  # list of nonisomorphic graphs
    for G in C:
        # check against all nonisomorphic graphs so far
        if not iso(G, nlist):
            nlist.append(G)
            UU = nx.disjoint_union(UU, G)  # union the nonisomorphic graphs
    return UU


def iso(G1, glist):
    """Quick and dirty nonisomorphism checker used to check isomorphisms."""
    for G2 in glist:
        if isomorphic(G1, G2):
            return True
    return False


if __name__ == '__main__':
    G = atlas6()

    print("graph has %d nodes with %d edges"
          % (nx.number_of_nodes(G), nx.number_of_edges(G)))
    print(nx.number_connected_components(G), "connected components")

    plt.figure(1, figsize=(8, 8))
    # layout graphs with positions using graphviz neato
    pos = graphviz_layout(G, prog="neato")
    # color nodes the same in each connected subgraph
    C = (G.subgraph(c) for c in nx.connected_components(G))
    for g in C:
        c = [random.random()] * nx.number_of_nodes(g)  # random color...
        nx.draw(g,
                pos,
                node_size=40,
                node_color=c,
                vmin=0.0,
                vmax=1.0,
                with_labels=False
               )
    plt.show()

graph has 779 nodes with 1073 edges
137 connected components

3. 图标Graph

• 空手道俱乐部Karate Club
• ER随机图Erdos Renyi
• 度序列Degree Sequence
• 足球football

## 空手道俱乐部Karate Club

import matplotlib.pyplot as plt
import networkx as nx

# 俱乐部数据
G = nx.karate_club_graph()
print("Node Degree")
for v in G:
    print('%s %s' % (v, G.degree(v)))

# 画环形，其中节点表示会员
nx.draw_circular(G, with_labels=True)
plt.show()

Node Degree
0 16
1 9
2 10
3 6
4 3
5 4
6 4
7 4
8 5
9 2
10 3
11 1
12 2
13 5
14 2
15 2
16 2
17 2
18 2
19 3
20 2
21 2
22 2
23 5
24 3
25 3
26 2
27 4
28 3
29 4
30 4
31 6
32 12
33 17

## ER随机图Erdos Renyi

# Author: Aric Hagberg ([email protected])

#    Copyright (C) 2004-2019 by
#    Aric Hagberg 
#    Dan Schult 
#    Pieter Swart 
#    All rights reserved.
#    BSD license.

import matplotlib.pyplot as plt
from networkx import nx

n = 10  # 10 nodes
m = 20  # 20 edges

G = nx.gnm_random_graph(n, m)

# some properties
print("node degree clustering")
for v in nx.nodes(G):
    print('%s %d %f' % (v, nx.degree(G, v), nx.clustering(G, v)))

# print the adjacency list
for line in nx.generate_adjlist(G):
    print(line)

nx.draw(G)
plt.show()

node degree clustering
0 2 1.000000
1 2 0.000000
2 3 0.333333
3 5 0.500000
4 5 0.500000
5 3 0.333333
6 4 0.500000
7 5 0.400000
8 5 0.300000
9 6 0.466667
0 9 8
1 7 6
2 4 5 8
3 7 4 9 5 6
4 6 9 7
5 8
6 9
7 9 8
8 9
9

## 度序列Degree Sequence

# Author: Aric Hagberg ([email protected])
# Date: 2004-11-03 08:11:09 -0700 (Wed, 03 Nov 2004)
# Revision: 503

#    Copyright (C) 2004-2019 by
#    Aric Hagberg 
#    Dan Schult 
#    Pieter Swart 
#    All rights reserved.
#    BSD license.

import matplotlib.pyplot as plt
from networkx import nx

z = [5, 3, 3, 3, 3, 2, 2, 2, 1, 1, 1]
print(nx.is_graphical(z))

print("Configuration model")
G = nx.configuration_model(z)  # configuration model
degree_sequence = [d for n, d in G.degree()]  # degree sequence
print("Degree sequence %s" % degree_sequence)
print("Degree histogram")
hist = {}
for d in degree_sequence:
    if d in hist:
        hist[d] += 1
    else:
        hist[d] = 1
print("degree #nodes")
for d in hist:
    print('%d %d' % (d, hist[d]))

nx.draw(G)
plt.show()

True
Configuration model
Degree sequence [5, 3, 3, 3, 3, 2, 2, 2, 1, 1, 1]
Degree histogram
degree #nodes
5 1
3 4
2 3
1 3

# 足球football

# Author: Aric Hagberg ([email protected])

#    Copyright (C) 2007-2019 by
#    Aric Hagberg 
#    Dan Schult 
#    Pieter Swart 
#    All rights reserved.
#    BSD license.

try:  # Python 3.x
    import urllib.request as urllib
except ImportError:  # Python 2.x
    import urllib
import io
import zipfile

import matplotlib.pyplot as plt
import networkx as nx

url = "http://www-personal.umich.edu/~mejn/netdata/football.zip"

sock = urllib.urlopen(url)  # open URL
s = io.BytesIO(sock.read())  # read into BytesIO "file"
sock.close()

zf = zipfile.ZipFile(s)  # zipfile object
txt = zf.read('football.txt').decode()  # read info file
gml = zf.read('football.gml').decode()  # read gml data
# throw away bogus first line with # from mejn files
gml = gml.split('\n')[1:]
G = nx.parse_gml(gml)  # parse gml data

#print(txt)
# print degree for each team - number of games
#for n, d in G.degree():
#    print('%s %d' % (n, d))

options = {
    'node_color': 'black',
    'node_size': 50,
    'line_color': 'grey',
    'linewidths': 0,
    'width': 0.1,
}
nx.draw(G, **options)
plt.show()