k_edge_subgraphs#
- k_edge_subgraphs(G, k)[source]#
Generates nodes in each maximal k-edge-connected subgraph in G.
- Parameters:
- GNetworkX graph
- kInteger
Desired edge connectivity
- Returns:
- k_edge_subgraphsa generator of k-edge-subgraphs
Each k-edge-subgraph is a maximal set of nodes that defines a subgraph of G that is k-edge-connected.
- Raises:
- NetworkXNotImplemented
If the input graph is a multigraph.
- ValueError:
If k is less than 1
See also
edge_connectivity()
k_edge_components()
similar to this function, but nodes only need to have k-edge-connctivity within the graph G and the subgraphs might not be k-edge-connected.
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.References
[1]Zhou, Liu, et al. (2012) Finding maximal k-edge-connected subgraphs from a large graph. ACM International Conference on Extending Database Technology 2012 480-â€“491. https://openproceedings.org/2012/conf/edbt/ZhouLYLCL12.pdf
Examples
>>> 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]]