def draw_network_value(orig_g, mG):
"""
Network values: The distribution of eigenvector components (indicators of "network value")
associated to the largest eigenvalue of the graph adjacency matrix has also been found to be
skewed (Chakrabarti et al., 2004).
"""
eig_cents = [nx.eigenvector_centrality_numpy(g) for g in mG] # nodes with eigencentrality
srt_eig_cents = sorted(eig_cents, reverse=True)
net_vals = []
for cntr in eig_cents:
net_vals.append(sorted(cntr.values(), reverse=True))
df = pd.DataFrame(net_vals)
plt.xscale('log')
plt.yscale('log')
plt.fill_between(df.columns, df.mean() - df.sem(), df.mean() + df.sem(), color='blue', alpha=0.2, label="se")
h, = plt.plot(df.mean(), color='blue', aa=True, linewidth=4, ls='--', label="H*")
orig, = plt.plot(sorted(nx.eigenvector_centrality(orig_g).values(), reverse=True), color='black', linewidth=4,
ls='-', label="H")
plt.title('Principle Eigenvector Distribution')
plt.ylabel('Principle Eigenvector')
plt.tick_params(
axis='x', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom='off', # ticks along the bottom edge are off
top='off', # ticks along the top edge are off
labelbottom='off') # labels along the bottom edge are off
plt.legend([orig, h], ['$H$', 'HRG $H^*$'], loc=3)
# fig = plt.gcf()
# fig.set_size_inches(5, 4, forward=True)
plt.show()
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