平行中间值

使用Python标准库中的多处理模块并行实现中间中心性的示例。

中间中心性函数接受一组节点,并计算这些节点对整个网络中间中心性的贡献。在这里,我们将网络划分为多个节点,并计算它们对整个网络中间中心性的贡献。

../../_images/sphx_glr_plot_parallel_betweenness_001.png

出:

Computing betweenness centrality for:
Name:
Type: Graph
Number of nodes: 1000
Number of edges: 2991
Average degree:   5.9820
        Parallel version
                Time: 3.4363
                Betweenness centrality for node 0: 0.00096
        Non-Parallel version
                Time: 3.8495 seconds
                Betweenness centrality for node 0: 0.00096

Computing betweenness centrality for:
Name:
Type: Graph
Number of nodes: 1000
Number of edges: 4887
Average degree:   9.7740
        Parallel version
                Time: 4.1927
                Betweenness centrality for node 0: 0.00382
        Non-Parallel version
                Time: 5.4284 seconds
                Betweenness centrality for node 0: 0.00382

Computing betweenness centrality for:
Name:
Type: Graph
Number of nodes: 1000
Number of edges: 2000
Average degree:   4.0000
        Parallel version
                Time: 3.4163
                Betweenness centrality for node 0: 0.00586
        Non-Parallel version
                Time: 3.5349 seconds
                Betweenness centrality for node 0: 0.00586

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_source,
        zip([G] * num_chunks, [True] * num_chunks, [None] * num_chunks, node_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("")
        print("Computing betweenness centrality for:")
        print(nx.info(G))
        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]))
    print("")

    nx.draw(G_ba)
    plt.show()

Total running time of the script: ( 0 minutes 31.318 seconds)

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