networkx.algorithms.tree.mst.minimum_spanning_tree¶

minimum_spanning_tree
(G, weight='weight', algorithm='kruskal', ignore_nan=False)[source]¶ Returns a minimum spanning tree or forest on an undirected graph
G
.Parameters:  G (undirected graph) – An undirected graph. If
G
is connected, then the algorithm finds a spanning tree. Otherwise, a spanning forest is found.  weight (str) – Data key to use for edge weights.
 algorithm (string) – The algorithm to use when finding a minimum spanning tree. Valid choices are ‘kruskal’, ‘prim’, or ‘boruvka’. The default is ‘kruskal’.
 ignore_nan (bool (default: False)) – If a NaN is found as an edge weight normally an exception is raised.
If
ignore_nan is True
then that edge is ignored instead.
Returns: G – A minimum spanning tree or forest.
Return type: NetworkX Graph
Examples
>>> G = nx.cycle_graph(4) >>> G.add_edge(0, 3, weight=2) >>> T = nx.minimum_spanning_tree(G) >>> sorted(T.edges(data=True)) [(0, 1, {}), (1, 2, {}), (2, 3, {})]
Notes
For Borůvka’s algorithm, each edge must have a weight attribute, and each edge weight must be distinct.
For the other algorithms, if the graph edges do not have a weight attribute a default weight of 1 will be used.
There may be more than one tree with the same minimum or maximum weight. See
networkx.tree.recognition
for more detailed definitions.Isolated nodes with selfloops are in the tree as edgeless isolated nodes.
 G (undirected graph) – An undirected graph. If