戴维斯俱乐部

戴维斯南方俱乐部女性

展示了如何制作图的单部分投影并计算这些图的属性。

这些数据是由戴维斯等人于20世纪30年代收集的。 它们代表了18位南方女性在14次社交活动中的观察出席情况。 该图是二部分的（俱乐部，女性）。

Biadjacency matrix
<Compressed Sparse Row sparse array of dtype 'int64'
        with 89 stored elements and shape (18, 14)>
  Coords        Values
  (0, 0)        1
  (0, 1)        1
  (0, 2)        1
  (0, 3)        1
  (0, 4)        1
  (0, 5)        1
  (0, 7)        1
  (0, 8)        1
  (1, 0)        1
  (1, 1)        1
  (1, 2)        1
  (1, 4)        1
  (1, 5)        1
  (1, 6)        1
  (1, 7)        1
  (2, 1)        1
  (2, 2)        1
  (2, 3)        1
  (2, 4)        1
  (2, 5)        1
  (2, 6)        1
  (2, 7)        1
  (2, 8)        1
  (3, 0)        1
  (3, 2)        1
  :     :
  (12, 7)       1
  (12, 8)       1
  (12, 9)       1
  (12, 11)      1
  (12, 12)      1
  (12, 13)      1
  (13, 5)       1
  (13, 6)       1
  (13, 8)       1
  (13, 9)       1
  (13, 10)      1
  (13, 11)      1
  (13, 12)      1
  (13, 13)      1
  (14, 6)       1
  (14, 7)       1
  (14, 9)       1
  (14, 10)      1
  (14, 11)      1
  (15, 7)       1
  (15, 8)       1
  (16, 8)       1
  (16, 10)      1
  (17, 8)       1
  (17, 10)      1

#Friends, Member
17 Evelyn Jefferson
15 Laura Mandeville
17 Theresa Anderson
15 Brenda Rogers
11 Charlotte McDowd
15 Frances Anderson
15 Eleanor Nye
16 Pearl Oglethorpe
17 Ruth DeSand
17 Verne Sanderson
16 Myra Liddel
16 Katherina Rogers
17 Sylvia Avondale
17 Nora Fayette
17 Helen Lloyd
16 Dorothy Murchison
12 Olivia Carleton
12 Flora Price

#Friend meetings, Member
50 Evelyn Jefferson
45 Laura Mandeville
57 Theresa Anderson
46 Brenda Rogers
24 Charlotte McDowd
32 Frances Anderson
36 Eleanor Nye
31 Pearl Oglethorpe
40 Ruth DeSand
38 Verne Sanderson
33 Myra Liddel
37 Katherina Rogers
46 Sylvia Avondale
43 Nora Fayette
34 Helen Lloyd
24 Dorothy Murchison
14 Olivia Carleton
14 Flora Price

import matplotlib.pyplot as plt
import networkx as nx
from networkx.algorithms import 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))

# 将二分图投影到女性节点上
W = bipartite.projected_graph(G, women)
print()
print("#Friends, Member")
for w in women:
    print(f"{W.degree(w)} {w}")

# 将二分图投影到女性节点上，保持共现次数
# 计算的度是加权的，并统计共享联系人的总数
W = bipartite.weighted_projected_graph(G, women)
print()
print("#Friend meetings, Member")
for w in women:
    print(f"{W.degree(w, weight='weight')} {w}")

pos = nx.spring_layout(G, seed=648)  # 为可重复的节点位置设置种子布局
nx.draw(G, pos)
plt.show()