Note

This documents the development version of NetworkX. Documentation for the current release can be found here.

networkx.algorithms.connectivity.kcomponents.k_components

k_components(G, flow_func=None)[source]

Returns the k-component structure of a graph G.

A k-component is a maximal subgraph of a graph G that has, at least, node connectivity k: we need to remove at least k nodes to break it into more components. k-components have an inherent hierarchical structure because they are nested in terms of connectivity: a connected graph can contain several 2-components, each of which can contain one or more 3-components, and so forth.

Parameters
GNetworkX graph
flow_funcfunction

Function to perform the underlying flow computations. Default value edmonds_karp(). This function performs better in sparse graphs with right tailed degree distributions. shortest_augmenting_path() will perform better in denser graphs.

Returns
k_componentsdict

Dictionary with all connectivity levels k in the input Graph as keys and a list of sets of nodes that form a k-component of level k as values.

Raises
NetworkXNotImplemented

If the input graph is directed.

See also

node_connectivity
all_node_cuts
biconnected_components

special case of this function when k=2

k_edge_components

similar to this function, but uses edge-connectivity instead of node-connectivity

Notes

Moody and White [1] (appendix A) provide an algorithm for identifying k-components in a graph, which is based on Kanevsky’s algorithm [2] for finding all minimum-size node cut-sets of a graph (implemented in all_node_cuts() function):

  1. Compute node connectivity, k, of the input graph G.

  2. Identify all k-cutsets at the current level of connectivity using Kanevsky’s algorithm.

  3. Generate new graph components based on the removal of these cutsets. Nodes in a cutset belong to both sides of the induced cut.

  4. If the graph is neither complete nor trivial, return to 1; else end.

This implementation also uses some heuristics (see [3] for details) to speed up the computation.

References

1

Moody, J. and D. White (2003). Social cohesion and embeddedness: A hierarchical conception of social groups. American Sociological Review 68(1), 103–28. http://www2.asanet.org/journals/ASRFeb03MoodyWhite.pdf

2

Kanevsky, A. (1993). Finding all minimum-size separating vertex sets in a graph. Networks 23(6), 533–541. http://onlinelibrary.wiley.com/doi/10.1002/net.3230230604/abstract

3

Torrents, J. and F. Ferraro (2015). Structural Cohesion: Visualization and Heuristics for Fast Computation. https://arxiv.org/pdf/1503.04476v1

Examples

>>> # Petersen graph has 10 nodes and it is triconnected, thus all
>>> # nodes are in a single component on all three connectivity levels
>>> G = nx.petersen_graph()
>>> k_components = nx.k_components(G)