minimum_branching(G, attr='weight', default=1, preserve_attrs=False, partition=None)

Parameters

G : (multi)digraph-like

The graph to be searched.

attr : str

The edge attribute used to in determining optimality.

default : float

The value of the edge attribute used if an edge does not have the attribute attr .

preserve_attrs : bool

If True, preserve the other attributes of the original graph (that are not passed to attr )

partition : str

The key for the edge attribute containing the partition data on the graph. Edges can be included, excluded or open using the EdgePartition enum.

Returns

B : (multi)digraph-like

A minimum branching.

Returns a minimum branching from G.

Examples

See :

Local connectivity graph

Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.

Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)

SVG is more flexible but power hungry; and does not scale well to 50 + nodes.

All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them

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GitHub : /networkx/algorithms/tree/branchings.py#726
type: <class 'function'>
Commit: