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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: 1.4551 seconds
Betweenness centrality for node 0: 0.01863
Non-Parallel version
Time: 6.4654 seconds
Betweenness centrality for node 0: 0.01863
Computing betweenness centrality for:
Name:
Type: Graph
Number of nodes: 1000
Number of edges: 5088
Average degree: 10.1760
Parallel version
Time: 1.7938 seconds
Betweenness centrality for node 0: 0.00281
Non-Parallel version
Time: 8.0934 seconds
Betweenness centrality for node 0: 0.00281
Computing betweenness centrality for:
Name:
Type: Graph
Number of nodes: 1000
Number of edges: 2000
Average degree: 4.0000
Parallel version
Time: 1.2444 seconds
Betweenness centrality for node 0: 0.00412
Non-Parallel version
Time: 5.5613 seconds
Betweenness centrality for node 0: 0.00412
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 40.629 seconds)