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node_disjoint_paths(G, s, t, flow_func=None, cutoff=None, auxiliary=None, residual=None)

Node disjoint paths are paths that only share their first and last nodes. The number of node independent paths between two nodes is equal to their local node connectivity.

Notes

This is a flow based implementation of node disjoint paths. We compute the maximum flow between source and target on an auxiliary directed network. The saturated edges in the residual network after running the maximum flow algorithm correspond to node disjoint paths between source and target in the original network. This function handles both directed and undirected graphs, and can use all flow algorithms from NetworkX flow package.

Parameters

G : NetworkX graph
s : node

Source node.

t : node

Target node.

flow_func : function

A function for computing the maximum flow among a pair of nodes. The function has to accept at least three parameters: a Digraph, a source node, and a target node. And return a residual network that follows NetworkX conventions (see maximum_flow for details). If flow_func is None, the default maximum flow function ( edmonds_karp ) is used. See below for details. The choice of the default function may change from version to version and should not be relied on. Default value: None.

cutoff : int

Maximum number of paths to yield. Some of the maximum flow algorithms, such as edmonds_karp (the default) and shortest_augmenting_path support the cutoff parameter, and will terminate when the flow value reaches or exceeds the cutoff. Other algorithms will ignore this parameter. Default value: None.

auxiliary : NetworkX DiGraph

Auxiliary digraph to compute flow based node connectivity. It has to have a graph attribute called mapping with a dictionary mapping node names in G and in the auxiliary digraph. If provided it will be reused instead of recreated. Default value: None.

residual : NetworkX DiGraph

Residual network to compute maximum flow. If provided it will be reused instead of recreated. Default value: None.

Raises

NetworkXNoPath

If there is no path between source and target.

NetworkXError

If source or target are not in the graph G.

Returns

paths : generator

Generator of node disjoint paths.

Computes node disjoint paths between source and target.

See Also

edge_disjoint_paths

meth

edmonds_karp

meth

maximum_flow

meth

node_connectivity

meth

preflow_push

meth

shortest_augmenting_path

meth

Examples

We use in this example the platonic icosahedral graph, which has node connectivity 5, thus there are 5 node disjoint paths between any pair of non neighbor nodes.

>>> G = nx.icosahedral_graph()
... len(list(nx.node_disjoint_paths(G, 0, 6))) 5

If you need to compute node disjoint paths between several pairs of nodes in the same graph, it is recommended that you reuse the data structures that NetworkX uses in the computation: the auxiliary digraph for node connectivity and node cuts, and the residual network for the underlying maximum flow computation.

Example of how to compute node disjoint paths reusing the data structures:

>>> # You also have to explicitly import the function for
... # building the auxiliary digraph from the connectivity package
... from networkx.algorithms.connectivity import build_auxiliary_node_connectivity
... H = build_auxiliary_node_connectivity(G)
... # And the function for building the residual network from the
... # flow package
... from networkx.algorithms.flow import build_residual_network
... # Note that the auxiliary digraph has an edge attribute named capacity
... R = build_residual_network(H, "capacity")
... # Reuse the auxiliary digraph and the residual network by passing them
... # as arguments
... len(list(nx.node_disjoint_paths(G, 0, 6, auxiliary=H, residual=R))) 5

You can also use alternative flow algorithms for computing node disjoint paths. For instance, in dense networks the algorithm shortest_augmenting_path will usually perform better than the default edmonds_karp which is faster for sparse networks with highly skewed degree distributions. Alternative flow functions have to be explicitly imported from the flow package.

>>> from networkx.algorithms.flow import shortest_augmenting_path
... len(list(nx.node_disjoint_paths(G, 0, 6, flow_func=shortest_augmenting_path))) 5
See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

networkx.algorithms.connectivity.disjoint_paths.edge_disjoint_paths networkx.algorithms.connectivity.disjoint_paths.node_disjoint_paths

Local connectivity graph

Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.

Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)

SVG is more flexible but power hungry; and does not scale well to 50 + nodes.

All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them


GitHub : /networkx/algorithms/connectivity/disjoint_paths.py#227
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