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: 2.1599 seconds
Betweenness centrality for node 0: 0.06075
Non-Parallel version
Time: 3.0811 seconds
Betweenness centrality for node 0: 0.06075
Computing betweenness centrality for:
Graph with 1000 nodes and 5068 edges
Parallel version
Time: 2.4538 seconds
Betweenness centrality for node 0: 0.00473
Non-Parallel version
Time: 4.2090 seconds
Betweenness centrality for node 0: 0.00473
Computing betweenness centrality for:
Graph with 1000 nodes and 2000 edges
Parallel version
Time: 1.7522 seconds
Betweenness centrality for node 0: 0.00234
Non-Parallel version
Time: 4.4488 seconds
Betweenness centrality for node 0: 0.00234
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 24.076 seconds)