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Note

This documents the development version of NetworkX. Documentation for the current release can be found here.

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networkx.algorithms.tree.mst.maximum_spanning_tree

maximum_spanning_tree(G, weight='weight', algorithm='kruskal', ignore_nan=False)[source]

Returns a maximum 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 maximum 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 maximum spanning tree or forest.

Return type

NetworkX Graph

Examples

>>> G = nx.cycle_graph(4)
>>> G.add_edge(0, 3, weight=2)
>>> T = nx.maximum_spanning_tree(G)
>>> sorted(T.edges(data=True))
[(0, 1, {}), (0, 3, {'weight': 2}), (1, 2, {})]

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 self-loops are in the tree as edgeless isolated nodes.