networkxで2部グラフ解析 - スペクトラルクラスタリング -

import networkx as nx
from networkx.algorithms import bipartite

import numpy as np
from sklearn.cluster import SpectralClustering
from sklearn import metrics
np.random.seed(1)

import matplotlib.pyplot as plt
%matplotlib inline

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

W = bipartite.weighted_projected_graph(G, women)

# as np matrix
adj_mat = nx.to_numpy_matrix(W)

# Cluster
sc = SpectralClustering(2, affinity='precomputed', n_init=100, assign_labels='discretize')
sc.fit(adj_mat)

print('spectral clustering')
print(sc.labels_)

plt.figure(figsize=(16, 10))
nx.draw_networkx(W)

plt.figure(figsize=(16, 10))
nx.draw_networkx(W, node_color=sc.labels_, cmap=plt.cm.rainbow, labels={d[0]: f'{i}: {d[0]}' for i, d in enumerate(W.nodes(data=True))})