Calculate centrality measures

Centrality is a thing with stuff and things.

# sphinx_gallery_thumbnail_number = 3

Start with the necessary imports

import os.path as op

import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt

import brainconn

Get some data

corr = np.loadtxt(op.join(brainconn.utils.get_resource_path(), 'example_corr.txt'))

# Zero diagonal
adj_wei = corr - np.eye(corr.shape[0])
adj_bin = brainconn.utils.binarize(brainconn.utils.threshold_proportional(adj_wei, 0.2))

Look at weighted adjacency matrix

fig, ax = plt.subplots(figsize=(7, 7))
ax.imshow(adj_wei)
fig.show()

Look at binary adjacency matrix

fig, ax = plt.subplots(figsize=(7, 7))
ax.imshow(adj_bin)
fig.show()

Compute stuff

betw_wei = brainconn.centrality.betweenness_wei(adj_wei)
betw_bin = brainconn.centrality.betweenness_bin(adj_bin)
edg_betw_wei = brainconn.centrality.edge_betweenness_wei(adj_wei)[0]
idx = np.triu_indices(edg_betw_wei.shape[0], k=1)
edg_betw_wei = edg_betw_wei[idx]
edg_betw_wei = edg_betw_wei[edg_betw_wei > 0]
edg_betw_bin = brainconn.centrality.edge_betweenness_bin(adj_bin)[0]
idx = np.triu_indices(edg_betw_bin.shape[0], k=1)
edg_betw_bin = edg_betw_bin[idx]
edg_betw_bin = edg_betw_bin[edg_betw_bin > 0]

vals = [betw_wei, betw_bin, edg_betw_wei, edg_betw_bin]
names = ['Weighted Node Betweenness Centrality',
         'Binary Node Betweenness Centrality',
         'Weighted Edge Betweenness Centrality',
         'Binary Edge Betweenness Centrality']
fig, axes = plt.subplots(nrows=4, figsize=(12, 7))
for i in range(4):
    sns.distplot(vals[i], ax=axes[i])
    axes[i].set_title(names[i])
fig.tight_layout()
fig.show()

Total running time of the script: ( 1 minutes 1.232 seconds)