k_edge_components#
- k_edge_components(G, k)[source]#
Generates nodes in each maximal k-edge-connected component in G.
- Parameters:
- GNetworkX graph
- kInteger
Desired edge connectivity
- Returns:
- k_edge_componentsa generator of k-edge-ccs. Each set of returned nodes
will have k-edge-connectivity in the graph G.
- Raises:
- NetworkXNotImplemented
If the input graph is a multigraph.
- ValueError:
If k is less than 1
See also
local_edge_connectivity()
k_edge_subgraphs()
similar to this function, but the subgraph defined by the nodes must also have k-edge-connectivity.
k_components()
similar to this function, but uses node-connectivity instead of edge-connectivity
Notes
Attempts to use the most efficient implementation available based on k. If k=1, this is simply connected components for directed graphs and connected components for undirected graphs. If k=2 on an efficient bridge connected component algorithm from _[1] is run based on the chain decomposition. Otherwise, the algorithm from _[2] is used.
References
[2]Wang, Tianhao, et al. (2015) A simple algorithm for finding all k-edge-connected components. http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0136264
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 returns {1, 4} unlike k_edge_subgraphs >>> sorted(map(sorted, nx.k_edge_components(G, k=3))) [[1, 4], [2], [3], [5, 6, 7, 8]]