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# node_disjoint_paths

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

  Computes node disjoint paths between source and target.

  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.

  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 integer or None (default: None)

      Maximum number of paths to yield. If specified, the maximum flow algorithm will terminate when the flow value reaches or exceeds the cutoff. This only works for flows that support the cutoff parameter (most do) and is ignored otherwise.

    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.

  Returns:
    paths generator

      Generator of node disjoint paths.

  Raises:
    NetworkXNoPath

      If there is no path between source and target.

    NetworkXError

      If source or target are not in the graph G.

  See also

  `edge_disjoint_paths()`
  `node_connectivity()`
  `maximum_flow()`
  `edmonds_karp()`
  `preflow_push()`
  `shortest_augmenting_path()`

  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.

  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