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 (hagberg@lanl.gov)

#    Copyright (C) 2004-2019 by

#    Aric Hagberg <hagberg@lanl.gov>

#    Dan Schult <dschult@colgate.edu>

#    Pieter Swart <swart@lanl.gov>

#    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 <hagberg@lanl.gov>

#    Dan Schult <dschult@colgate.edu>

#    Pieter Swart <swart@lanl.gov>

#    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 (hagberg@lanl.gov)

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 (hagberg@lanl.gov)

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 (hagberg@lanl.gov)

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 <aric.hagberg@gmail.com>

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 (hagberg@lanl.gov)

#    Copyright (C) 2004-2019

#    Aric Hagberg <hagberg@lanl.gov>

#    Dan Schult <dschult@colgate.edu>

#    Pieter Swart <swart@lanl.gov>

#    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 (drew.conway@nyu.edu)

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 (hagberg@lanl.gov)

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 (rodgdor@gmail.com)

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 (hagberg@lanl.gov)

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 <hagberg@lanl.gov>

#    Dan Schult <dschult@colgate.edu>

#    Pieter Swart <swart@lanl.gov>

#    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 (hagberg@lanl.gov)

#    Copyright (C) 2004-2019 by

#    Aric Hagberg <hagberg@lanl.gov>

#    Dan Schult <dschult@colgate.edu>

#    Pieter Swart <swart@lanl.gov>

#    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 (hagberg@lanl.gov)

#    Copyright (C) 2004-2019 by

#    Aric Hagberg <hagberg@lanl.gov>

#    Dan Schult <dschult@colgate.edu>

#    Pieter Swart <swart@lanl.gov>

#    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 (hagberg@lanl.gov)

# Date: 2004-11-03 08:11:09 -0700 (Wed, 03 Nov 2004)

# Revision: 503

#    Copyright (C) 2004-2019 by

#    Aric Hagberg <hagberg@lanl.gov>

#    Dan Schult <dschult@colgate.edu>

#    Pieter Swart <swart@lanl.gov>

#    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 (hagberg@lanl.gov)

#    Copyright (C) 2007-2019 by

#    Aric Hagberg <hagberg@lanl.gov>

#    Dan Schult <dschult@colgate.edu>

#    Pieter Swart <swart@lanl.gov>

#    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()

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