小编Mag*_*dou的帖子

我注意到，如果我使用相同的值更改图中的所有边权重，则community.best_partition并不总是导致相同的社区。

我在所有情况下都使用相同的随机状态，图形完全相同，只是不是所有边权重都等于 1，例如它们可能等于 5。模块化的定义抵消了这个乘以邻接矩阵的因素当我阅读算法时，我找不到应该改变结果的步骤。是否有导致这种差异的原因？

import networkx as nx
import community

from sklearn.metrics import adjusted_rand_score


def main():
    g = nx.davis_southern_women_graph()
    nodes = g.nodes()
    clusters_init = community.best_partition(g, random_state=10)
    print("modularity with initial clusters = %.15f" % community.modularity(clusters_init, g))

    labels_init = [clusters_init[n] for n in nodes]
    for num in range(1, 9):

        for u, v in g.edges():
            g[u][v]["weight"] = num

        clusters = community.best_partition(g, random_state=10)
        labels = [clusters[n] for n in nodes]
        print("value of edge weight = %d," % num, "modularity = %.15f," % …