find_optimum(self, attr='weight', default=1, kind='max', style='branching', preserve_attrs=False, partition=None, seed=None)

Parameters

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 .

kind : {'min', 'max'}

The type of optimum to search for, either 'min' or 'max'.

style : {'branching', 'arborescence'}

If 'branching', then an optimal branching is found. If style is 'arborescence', then a branching is found, such that if the branching is also an arborescence, then the branching is an optimal spanning arborescences. A given graph G need not have an optimal spanning arborescence.

preserve_attrs : bool

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

partition : str

The edge attribute holding edge partition data. Used in the spanning arborescence iterator.

seed : integer, random_state, or None (default)

Indicator of random number generation state. See Randomness<randomness> .

Returns

H : (multi)digraph

The branching.

Returns a 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#387
type: <class 'function'>
Commit: