Betweenness centrality is a type of centrality similar to closeness centrality (refer to the Calculating social network closeness centrality recipe). This metric is given by the following equation:
It is the total of the fraction of all possible pairs of shortest paths that go through a node.
The script is in the between_centrality.ipynb
file in this book's code bundle:
import networkx as nx import dautil as dl import pandas as pd
fb_file = dl.data.SPANFB().load() G = nx.read_edgelist(fb_file, create_using=nx.Graph(), nodetype=int)
k = 256
(number of nodes to use) and store the result in a pandas DataFrame
object:key_values = nx.betweenness_centrality(G, k=256) df = pd.DataFrame.from_dict(key_values, orient='index') dl.options.set_pd_options() print('Betweenness Centrality', df)
Refer to the following screenshot for the end result:
betweenness_centrality()
function at https://networkx.github.io/documentation/latest/reference/generated/networkx.algorithms.centrality.betweenness_centrality.html (retrieved October 2015)