import numpy as np
import networkx as nx
import numpy.linalg as la
import scipy.cluster.vq as vq
import matplotlib.pyplot as plt

G = nx.powerlaw_cluster_graph(100, 1, 0.0)
coord = nx.spring_layout(G, iterations=1000)


fig = plt.figure()
axs = fig.add_subplot(111, aspect='equal')
axs.axis('off')
nx.draw_networkx_edges(G, coord)
nx.draw_networkx_nodes(G, coord, node_size=45, node_color='k')
plt.show()


A = nx.adjacency_matrix(G)
D = np.diag(np.ravel(np.sum(A, axis=1)))
L = D - A

l, U = la.eigh(L)
f = U[:,1]

# labels = np.ravel(np.sign(f))
k = 3
means, labels = vq.kmeans2(U[:,1:k], k)

fig = plt.figure()
nx.draw_networkx_edges(G, coord)
nx.draw_networkx_nodes(G, coord, node_size=45, node_color=labels)
plt.show()

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