edge_betweenness_partition#

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

Partition created by iteratively removing the highest edge betweenness edge.

This algorithm works by calculating the edge betweenness for all edges and removing the edge with the highest value. It is then determined whether the graph has been broken into at least number_of_sets connected components. If not the process is repeated.

Parameters:

GNetworkX Graph, DiGraph or MultiGraph: Graph to be partitioned
number_of_setsint: Number of sets in the desired partition of the graph
weightkey, optional, default=None: The key to use if using weights for edge betweenness calculation

Returns:

Clist of sets: Partition of the nodes of G

Raises:

NetworkXError: If number_of_sets is <= 0 or if number_of_sets > len(G)

See also

edge_current_flow_betweenness_partition

Notes

This algorithm is fairly slow, as both the calculation of connected components and edge betweenness relies on all pairs shortest path algorithms. They could potentially be combined to cut down on overall computation time.

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