scipy.sparse.csgraph.minimum_spanning_tree¶

scipy.sparse.csgraph.
minimum_spanning_tree
(csgraph, overwrite=False)¶ Return a minimum spanning tree of an undirected graph
A minimum spanning tree is a graph consisting of the subset of edges which together connect all connected nodes, while minimizing the total sum of weights on the edges. This is computed using the Kruskal algorithm.
New in version 0.11.0.
 Parameters
 csgrapharray_like or sparse matrix, 2 dimensions
The N x N matrix representing an undirected graph over N nodes (see notes below).
 overwritebool, optional
if true, then parts of the input graph will be overwritten for efficiency.
 Returns
 span_treecsr matrix
The N x N compressedsparse representation of the undirected minimum spanning tree over the input (see notes below).
Notes
This routine uses undirected graphs as input and output. That is, if graph[i, j] and graph[j, i] are both zero, then nodes i and j do not have an edge connecting them. If either is nonzero, then the two are connected by the minimum nonzero value of the two.
This routine loses precision when users input a dense matrix. Small elements < 1E8 of the dense matrix are rounded to zero. All users should input sparse matrices if possible to avoid it.
Examples
The following example shows the computation of a minimum spanning tree over a simple fourcomponent graph:
input graph minimum spanning tree (0) (0) / \ / 3 8 3 / \ / (3)5(1) (3)5(1) \ / / 6 2 2 \ / / (2) (2)
It is easy to see from inspection that the minimum spanning tree involves removing the edges with weights 8 and 6. In compressed sparse representation, the solution looks like this:
>>> from scipy.sparse import csr_matrix >>> from scipy.sparse.csgraph import minimum_spanning_tree >>> X = csr_matrix([[0, 8, 0, 3], ... [0, 0, 2, 5], ... [0, 0, 0, 6], ... [0, 0, 0, 0]]) >>> Tcsr = minimum_spanning_tree(X) >>> Tcsr.toarray().astype(int) array([[0, 0, 0, 3], [0, 0, 2, 5], [0, 0, 0, 0], [0, 0, 0, 0]])