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Parallel BetweennessΒΆ
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.

Out:
Computing betweenness centrality for:
Name:
Type: Graph
Number of nodes: 1000
Number of edges: 2991
Average degree: 5.9820
Parallel version
Time: 4.5416 seconds
Betweenness centrality for node 0: 0.07088
Non-Parallel version
Time: 4.1826 seconds
Betweenness centrality for node 0: 0.07088
Computing betweenness centrality for:
Name:
Type: Graph
Number of nodes: 1000
Number of edges: 5005
Average degree: 10.0100
Parallel version
Time: 5.8073 seconds
Betweenness centrality for node 0: 0.00148
Non-Parallel version
Time: 5.2929 seconds
Betweenness centrality for node 0: 0.00148
Computing betweenness centrality for:
Name:
Type: Graph
Number of nodes: 1000
Number of edges: 2000
Average degree: 4.0000
Parallel version
Time: 3.9722 seconds
Betweenness centrality for node 0: 0.00163
Non-Parallel version
Time: 3.6879 seconds
Betweenness centrality for node 0: 0.00163
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(), int(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(nx.info(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 34.052 seconds)