edge_current_flow_betweenness_partition#

edge_current_flow_betweenness_partition(G, number_of_sets, *, weight=None)[source]#

通过移除具有最高边介数流量的边来创建分区。

该算法通过计算所有边的边介数流量，并移除具有最高值的边来工作。然后确定图是否已被分割成至少 number_of_sets 个连通分量。如果没有，则重复此过程。

Parameters:

GNetworkX 图、有向图或多重图: 需要分区的图
number_of_setsint: 图分区中所需集合的数量
weight键, 可选 (默认=None): 用作边介数流量计算权重的边属性键

Returns:

C集合列表: G的分区

Raises:

NetworkXError: 如果 number_of_sets <= 0 或 number_of_sets > len(G)

See also

edge_betweenness_partition

Notes

该算法非常慢，因为重新计算边介数流量极其缓慢。

References

[1]

Santo Fortunato ‘Community Detection in Graphs’ Physical Reports Volume 486, Issue 3-5 p. 75-174 http://arxiv.org/abs/0906.0612

Examples

>>> G = nx.karate_club_graph()
>>> part = nx.community.edge_current_flow_betweenness_partition(G, 2)
>>> {0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 16, 17, 19, 21} in part
True
>>> {8, 14, 15, 18, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33} in part
True