# 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: 1.7025 seconds
Betweenness centrality for node 0: 0.08967
Non-Parallel version
Time: 2.8166 seconds
Betweenness centrality for node 0: 0.08967

Computing betweenness centrality for:
Graph with 1000 nodes and 4955 edges
Parallel version
Time: 2.1225 seconds
Betweenness centrality for node 0: 0.00104
Non-Parallel version
Time: 3.6881 seconds
Betweenness centrality for node 0: 0.00104

Computing betweenness centrality for:
Graph with 1000 nodes and 2000 edges
Parallel version
Time: 1.4960 seconds
Betweenness centrality for node 0: 0.00211
Non-Parallel version
Time: 2.5306 seconds
Betweenness centrality for node 0: 0.00211
```

```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(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 19.769 seconds)

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