`scipy.sparse.csgraph._traversal:breadth_first_tree`

Signature

def breadth_first_tree(csgraph, i_start, directed = True)

Summary

Return the tree generated by a breadth-first search.

Extended Summary

Note that a breadth-first tree from a specified node is unique.

Parameters

csgraph : array_like or sparse array or matrix: The N x N matrix representing the compressed sparse graph. The input csgraph will be converted to csr format for the calculation.
i_start : int: The index of starting node.
directed : bool, optional: If True (default), then operate on a directed graph: only move from point i to point j along paths csgraph[i, j]. If False, then find the shortest path on an undirected graph: the algorithm can progress from point i to j along csgraph[i, j] or csgraph[j, i].

Returns

cstree : csr matrix: The N x N directed compressed-sparse representation of the breadth- first tree drawn from csgraph, starting at the specified node.

Notes

If multiple valid solutions are possible, output may vary with SciPy and Python version.

Examples

The following example shows the computation of a depth-first tree over a simple four-component graph, starting at node 0:: input graph breadth first tree from (0) (0) (0) / \ / \ 3 8 3 8 / \ / \ (3)---5---(1) (3) (1) \ / / 6 2 2 \ / / (2) (2) In compressed sparse representation, the solution looks like this:

from scipy.sparse import csr_array
from scipy.sparse.csgraph import breadth_first_tree
X = csr_array([[0, 8, 0, 3],
               [0, 0, 2, 5],
               [0, 0, 0, 6],
               [0, 0, 0, 0]])
Tcsr = breadth_first_tree(X, 0, directed=False)
Tcsr.toarray().astype(int)

✓

array([[0, 8, 0, 3],
       [0, 0, 2, 0],
       [0, 0, 0, 0],
       [0, 0, 0, 0]])

Note that the resulting graph is a Directed Acyclic Graph which spans the graph. A breadth-first tree from a given node is unique.

Aliases

scipy.sparse.csgraph.breadth_first_tree