戴维斯俱乐部、空手道俱乐部、拿破仑俄国战役数据实现。

import matplotlib.pyplot as plt
import networkx as nx
import networkx.algorithms.bipartite as bipartite

G = nx.davis_southern_women_graph()
women = G.graph['top']
clubs = G.graph['bottom']

print("Biadjacency matrix")
print(bipartite.biadjacency_matrix(G, women, clubs))

# project bipartite graph onto women nodes
W = bipartite.projected_graph(G, women)
print('')
print("#Friends, Member")
for w in women:
    print('%d %s' % (W.degree(w), w))

# project bipartite graph onto women nodes keeping number of co-occurence
# the degree computed is weighted and counts the total number of shared contacts
W = bipartite.weighted_projected_graph(G, women)
print('')
print("#Friend meetings, Member")
for w in women:
    print('%d %s' % (W.degree(w, weight='weight'), w))

nx.draw(G)
plt.show()
import matplotlib.pyplot as plt
import networkx as nx

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

nx.draw_circular(G, with_labels=True)
plt.show()
import matplotlib.pyplot as plt
import networkx as nx


def minard_graph():
    data1 = """\
24.0,54.9,340000,A,1
24.5,55.0,340000,A,1
25.5,54.5,340000,A,1
26.0,54.7,320000,A,1
27.0,54.8,300000,A,1
28.0,54.9,280000,A,1
28.5,55.0,240000,A,1
29.0,55.1,210000,A,1
30.0,55.2,180000,A,1
30.3,55.3,175000,A,1
32.0,54.8,145000,A,1
33.2,54.9,140000,A,1
34.4,55.5,127100,A,1
35.5,55.4,100000,A,1
36.0,55.5,100000,A,1
37.6,55.8,100000,A,1
37.7,55.7,100000,R,1
37.5,55.7,98000,R,1
37.0,55.0,97000,R,1
36.8,55.0,96000,R,1
35.4,55.3,87000,R,1
34.3,55.2,55000,R,1
33.3,54.8,37000,R,1
32.0,54.6,24000,R,1
30.4,54.4,20000,R,1
29.2,54.3,20000,R,1
28.5,54.2,20000,R,1
28.3,54.3,20000,R,1
27.5,54.5,20000,R,1
26.8,54.3,12000,R,1
26.4,54.4,14000,R,1
25.0,54.4,8000,R,1
24.4,54.4,4000,R,1
24.2,54.4,4000,R,1
24.1,54.4,4000,R,1"""
    data2 = """\
24.0,55.1,60000,A,2
24.5,55.2,60000,A,2
25.5,54.7,60000,A,2
26.6,55.7,40000,A,2
27.4,55.6,33000,A,2
28.7,55.5,33000,R,2
29.2,54.2,30000,R,2
28.5,54.1,30000,R,2
28.3,54.2,28000,R,2"""
    data3 = """\
24.0,55.2,22000,A,3
24.5,55.3,22000,A,3
24.6,55.8,6000,A,3
24.6,55.8,6000,R,3
24.2,54.4,6000,R,3
24.1,54.4,6000,R,3"""
    cities = """\
24.0,55.0,Kowno
25.3,54.7,Wilna
26.4,54.4,Smorgoni
26.8,54.3,Moiodexno
27.7,55.2,Gloubokoe
27.6,53.9,Minsk
28.5,54.3,Studienska
28.7,55.5,Polotzk
29.2,54.4,Bobr
30.2,55.3,Witebsk
30.4,54.5,Orscha
30.4,53.9,Mohilow
32.0,54.8,Smolensk
33.2,54.9,Dorogobouge
34.3,55.2,Wixma
34.4,55.5,Chjat
36.0,55.5,Mojaisk
37.6,55.8,Moscou
36.6,55.3,Tarantino
36.5,55.0,Malo-Jarosewii"""

    c = {}
    for line in cities.split('\n'):
        x, y, name = line.split(',')
        c[name] = (float(x), float(y))

    g = []

    for data in [data1, data2, data3]:
        G = nx.Graph()
        i = 0
        G.pos = {}  # location
        G.pop = {}  # size
        last = None
        for line in data.split('\n'):
            x, y, p, r, n = line.split(',')
            G.pos[i] = (float(x), float(y))
            G.pop[i] = int(p)
            if last is None:
                last = i
            else:
                G.add_edge(i, last, **{r: int(n)})
                last = i
            i = i + 1
        g.append(G)

    return g, c


if __name__ == "__main__":

    (g, city) = minard_graph()

    plt.figure(1, figsize=(11, 5))
    plt.clf()
    colors = ['b', 'g', 'r']
    for G in g:
        c = colors.pop(0)
        node_size = [int(G.pop[n] / 300.0) for n in G]
        nx.draw_networkx_edges(G, G.pos, edge_color=c, width=4, alpha=0.5)
        nx.draw_networkx_nodes(G, G.pos, node_size=node_size, node_color=c, alpha=0.5)
        nx.draw_networkx_nodes(G, G.pos, node_size=5, node_color='k')

    for c in city:
        x, y = city[c]
        plt.text(x, y + 0.1, c)
    plt.show()