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-rw-r--r--networkx/algorithms/tree/mst.py4
1 files changed, 2 insertions, 2 deletions
diff --git a/networkx/algorithms/tree/mst.py b/networkx/algorithms/tree/mst.py
index 8ec693d6..d36f411b 100644
--- a/networkx/algorithms/tree/mst.py
+++ b/networkx/algorithms/tree/mst.py
@@ -613,7 +613,7 @@ def partition_spanning_tree(
"""
Find a spanning tree while respecting a partition of edges.
- Edges can be flagged as either `INLCUDED` which are required to be in the
+ Edges can be flagged as either `INCLUDED` which are required to be in the
returned tree, `EXCLUDED`, which cannot be in the returned tree and `OPEN`.
This is used in the SpanningTreeIterator to create new partitions following
@@ -742,7 +742,7 @@ def random_spanning_tree(G, weight=None, *, multiplicative=True, seed=None):
is based on the product of edge weights, and if ``multiplicative=False``
it is based on the sum of the edge weight. However, since it is
easier to determine the total weight of all spanning trees for the
- multiplicative verison, that is significantly faster and should be used if
+ multiplicative version, that is significantly faster and should be used if
possible. Additionally, setting `weight` to `None` will cause a spanning tree
to be selected with uniform probability.