spectral_bipartivity(G, nodes=None, weight='weight')

Notes

This implementation uses Numpy (dense) matrices which are not efficient for storing large sparse graphs.

Parameters

G : NetworkX graph

nodes : list or container optional(default is all nodes)

Nodes to return value of spectral bipartivity contribution.

weight : string or None optional (default = 'weight')

Edge data key to use for edge weights. If None, weights set to 1.

Returns

sb : float or dict

A single number if the keyword nodes is not specified, or a dictionary keyed by node with the spectral bipartivity contribution of that node as the value.

Returns the spectral bipartivity.

See Also

color

Examples

>>> from networkx.algorithms import bipartite
... G = nx.path_graph(4)
... bipartite.spectral_bipartivity(G)
1.0

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

networkx.algorithms.bipartite.spectral.spectral_bipartivity

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/bipartite/spectral.py#9
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