# networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph¶

class EdgeComponentAuxGraph[source]

A simple algorithm to find all k-edge-connected components in a graph.

Constructing the AuxillaryGraph (which may take some time) allows for the k-edge-ccs to be found in linear time for arbitrary k.

Notes

This implementation is based on [1]. The idea is to construct an auxiliary graph from which the k-edge-ccs can be extracted in linear time. The auxiliary graph is constructed in $$O(|V|\cdot F)$$ operations, where F is the complexity of max flow. Querying the components takes an additional $$O(|V|)$$ operations. This algorithm can be slow for large graphs, but it handles an arbitrary k and works for both directed and undirected inputs.

The undirected case for k=1 is exactly connected components. The undirected case for k=2 is exactly bridge connected components. The directed case for k=1 is exactly strongly connected components.

References

1

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
>>> from networkx.algorithms.connectivity import EdgeComponentAuxGraph
>>> # Build an interesting graph with multiple levels of k-edge-ccs
>>> paths = [
...     (1, 2, 3, 4, 1, 3, 4, 2),  # a 3-edge-cc (a 4 clique)
...     (5, 6, 7, 5),  # a 2-edge-cc (a 3 clique)
...     (1, 5),  # combine first two ccs into a 1-edge-cc
... ]
>>> G = nx.Graph()
>>> G.add_edges_from(it.chain(*[pairwise(path) for path in paths]))
>>> # Constructing the AuxGraph takes about O(n ** 4)
>>> aux_graph = EdgeComponentAuxGraph.construct(G)
>>> # Once constructed, querying takes O(n)
>>> sorted(map(sorted, aux_graph.k_edge_components(k=1)))
[[0], [1, 2, 3, 4, 5, 6, 7]]
>>> sorted(map(sorted, aux_graph.k_edge_components(k=2)))
[[0], [1, 2, 3, 4], [5, 6, 7]]
>>> sorted(map(sorted, aux_graph.k_edge_components(k=3)))
[[0], [1, 2, 3, 4], [5], [6], [7]]
>>> sorted(map(sorted, aux_graph.k_edge_components(k=4)))
[[0], [1], [2], [3], [4], [5], [6], [7]]


The auxiliary graph is primarilly used for k-edge-ccs but it can also speed up the queries of k-edge-subgraphs by refining the search space.

>>> import itertools as it
>>> from networkx.utils import pairwise
>>> from networkx.algorithms.connectivity import EdgeComponentAuxGraph
>>> paths = [
...     (1, 2, 4, 3, 1, 4),
... ]
>>> G = nx.Graph()
>>> G.add_edges_from(it.chain(*[pairwise(path) for path in paths]))
>>> aux_graph = EdgeComponentAuxGraph.construct(G)
>>> sorted(map(sorted, aux_graph.k_edge_subgraphs(k=3)))
[[1], [2], [3], [4]]
>>> sorted(map(sorted, aux_graph.k_edge_components(k=3)))
[[1, 4], [2], [3]]

__init__(*args, **kwargs)

Initialize self. See help(type(self)) for accurate signature.

Methods

 __init__(*args, **kwargs) Initialize self. construct(G) Builds an auxiliary graph encoding edge-connectivity between nodes. k_edge_components(k) Queries the auxiliary graph for k-edge-connected components. k_edge_subgraphs(k) Queries the auxiliary graph for k-edge-connected subgraphs.