Note
Click here to download the full example code
Parallel Betweenness#
Example of parallel implementation of betweenness centrality using the multiprocessing module from Python Standard Library.
The function betweenness centrality accepts a bunch of nodes and computes the contribution of those nodes to the betweenness centrality of the whole network. Here we divide the network in chunks of nodes and we compute their contribution to the betweenness centrality of the whole network.
Note: The example output below shows that the non-parallel implementation is faster. This is a limitation of our CI/CD pipeline running on a single core.
Depending on your setup, you will likely observe a speedup.

Computing betweenness centrality for:
Graph with 1000 nodes and 2991 edges
Parallel version
Time: 1.4022 seconds
Betweenness centrality for node 0: 0.17088
Non-Parallel version
Time: 2.4262 seconds
Betweenness centrality for node 0: 0.17088
Computing betweenness centrality for:
Graph with 1000 nodes and 4987 edges
Parallel version
Time: 1.8718 seconds
Betweenness centrality for node 0: 0.00289
Non-Parallel version
Time: 3.2627 seconds
Betweenness centrality for node 0: 0.00289
Computing betweenness centrality for:
Graph with 1000 nodes and 2000 edges
Parallel version
Time: 1.3322 seconds
Betweenness centrality for node 0: 0.00096
Non-Parallel version
Time: 2.2473 seconds
Betweenness centrality for node 0: 0.00096
from multiprocessing import Pool
import time
import itertools
import matplotlib.pyplot as plt
import networkx as nx
def chunks(l, n):
"""Divide a list of nodes `l` in `n` chunks"""
l_c = iter(l)
while 1:
x = tuple(itertools.islice(l_c, n))
if not x:
return
yield x
def betweenness_centrality_parallel(G, processes=None):
"""Parallel betweenness centrality function"""
p = Pool(processes=processes)
node_divisor = len(p._pool) * 4
node_chunks = list(chunks(G.nodes(), G.order() // node_divisor))
num_chunks = len(node_chunks)
bt_sc = p.starmap(
nx.betweenness_centrality_subset,
zip(
[G] * num_chunks,
node_chunks,
[list(G)] * num_chunks,
[True] * num_chunks,
[None] * num_chunks,
),
)
# Reduce the partial solutions
bt_c = bt_sc[0]
for bt in bt_sc[1:]:
for n in bt:
bt_c[n] += bt[n]
return bt_c
G_ba = nx.barabasi_albert_graph(1000, 3)
G_er = nx.gnp_random_graph(1000, 0.01)
G_ws = nx.connected_watts_strogatz_graph(1000, 4, 0.1)
for G in [G_ba, G_er, G_ws]:
print("")
print("Computing betweenness centrality for:")
print(G)
print("\tParallel version")
start = time.time()
bt = betweenness_centrality_parallel(G)
print(f"\t\tTime: {(time.time() - start):.4F} seconds")
print(f"\t\tBetweenness centrality for node 0: {bt[0]:.5f}")
print("\tNon-Parallel version")
start = time.time()
bt = nx.betweenness_centrality(G)
print(f"\t\tTime: {(time.time() - start):.4F} seconds")
print(f"\t\tBetweenness centrality for node 0: {bt[0]:.5f}")
print("")
nx.draw(G_ba, node_size=100)
plt.show()
Total running time of the script: ( 0 minutes 17.346 seconds)