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greedy_branching(G, attr='weight', default=1, kind='max', seed=None)

This algorithm is wrong, and cannot give a proper optimal branching. However, we include it for pedagogical reasons, as it can be helpful to see what its outputs are.

The output is a branching, and possibly, a spanning arborescence. However, it is not guaranteed to be optimal in either case.

Parameters

G : DiGraph

The directed graph to scan.

attr : str

The attribute to use as weights. If None, then each edge will be treated equally with a weight of 1.

default : float

When attr is not None, then if an edge does not have that attribute, default specifies what value it should take.

kind : str

The type of optimum to search for: 'min' or 'max' greedy branching.

seed : integer, random_state, or None (default)

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

Returns

B : directed graph

The greedily obtained branching.

Returns a branching obtained through a greedy algorithm.

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


GitHub : /networkx/algorithms/tree/branchings.py#108
type: <class 'function'>
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