networkx.algorithms.connectivity.edge_kcomponents.k_edge_subgraphs¶

k_edge_subgraphs
(G, k)[source]¶ Generates nodes in each maximal kedgeconnected subgraph in G.
 Parameters
G (NetworkX graph)
k (Integer) – Desired edge connectivity
 Returns
k_edge_subgraphs – Each kedgesubgraph is a maximal set of nodes that defines a subgraph of G that is kedgeconnected.
 Return type
a generator of kedgesubgraphs
See also
edge_connectivity()
k_edge_components()
similar to this function, but nodes only need to have kedgeconnctivity within the graph G and the subgraphs might not be kedgeconnected.
 Raises
NetworkXNotImplemented – If the input graph is a multigraph.
ValueError: – If k is less than 1
Notes
Attempts to use the most efficient implementation available based on k. If k=1, or k=2 and the graph is undirected, then this simply calls
k_edge_components
. Otherwise the algorithm from _[1] is used.Example
>>> import itertools as it >>> from networkx.utils import pairwise >>> paths = [ ... (1, 2, 4, 3, 1, 4), ... (5, 6, 7, 8, 5, 7, 8, 6), ... ] >>> G = nx.Graph() >>> G.add_nodes_from(it.chain(*paths)) >>> G.add_edges_from(it.chain(*[pairwise(path) for path in paths])) >>> # note this does not return {1, 4} unlike k_edge_components >>> sorted(map(sorted, nx.k_edge_subgraphs(G, k=3))) [[1], [2], [3], [4], [5, 6, 7, 8]]
References
 1
Zhou, Liu, et al. (2012) Finding maximal kedgeconnected subgraphs from a large graph. ACM International Conference on Extending Database Technology 2012 480–491. https://openproceedings.org/2012/conf/edbt/ZhouLYLCL12.pdf