Note
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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: 1.2318 seconds
Betweenness centrality for node 0: 0.15503
Non-Parallel version
Time: 2.1112 seconds
Betweenness centrality for node 0: 0.15503
Computing betweenness centrality for:
Graph with 1000 nodes and 4872 edges
Parallel version
Time: 1.6046 seconds
Betweenness centrality for node 0: 0.00243
Non-Parallel version
Time: 2.7994 seconds
Betweenness centrality for node 0: 0.00243
Computing betweenness centrality for:
Graph with 1000 nodes and 2000 edges
Parallel version
Time: 1.0858 seconds
Betweenness centrality for node 0: 0.00722
Non-Parallel version
Time: 1.8483 seconds
Betweenness centrality for node 0: 0.00722
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 15.635 seconds)