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.

This doesn’t work in python2.7.13. It does work in 3.6, 3.5, 3.4, and 3.3.

It may be related to this:

Traceback (most recent call last):
  File "/home/travis/build/networkx/networkx/examples/advanced/", line 77, in <module>
    bt = betweenness_centrality_parallel(G)
  File "/home/travis/build/networkx/networkx/examples/advanced/", line 57, in betweenness_centrality_parallel
  File "/opt/python/2.7.14/lib/python2.7/multiprocessing/", line 253, in map
    return self.map_async(func, iterable, chunksize).get()
  File "/opt/python/2.7.14/lib/python2.7/multiprocessing/", line 572, in get
    raise self._value
PicklingError: Can't pickle <type 'function'>: attribute lookup __builtin__.function failed
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:
        yield x

def _betmap(G_normalized_weight_sources_tuple):
    """Pool for multiprocess only accepts functions with one argument.
    This function uses a tuple as its only argument. We use a named tuple for
    python 3 compatibility, and then unpack it when we send it to
    return nx.betweenness_centrality_source(*G_normalized_weight_sources_tuple)

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 =,
                  zip([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

if __name__ == "__main__":
    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("Computing betweenness centrality for:")
        print("\tParallel version")
        start = time.time()
        bt = betweenness_centrality_parallel(G)
        print("\t\tTime: %.4F" % (time.time() - start))
        print("\t\tBetweenness centrality for node 0: %.5f" % (bt[0]))
        print("\tNon-Parallel version")
        start = time.time()
        bt = nx.betweenness_centrality(G)
        print("\t\tTime: %.4F seconds" % (time.time() - start))
        print("\t\tBetweenness centrality for node 0: %.5f" % (bt[0]))


Total running time of the script: ( 0 minutes 0.000 seconds)

Gallery generated by Sphinx-Gallery