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from_biadjacency_matrix(A, create_using=None, edge_attribute='weight')

Notes

The nodes are labeled with the attribute bipartite set to an integer 0 or 1 representing membership in part 0 or part 1 of the bipartite graph.

If :None:None:`create_using` is an instance of networkx.MultiGraph or networkx.MultiDiGraph and the entries of A are of type int , then this function returns a multigraph (of the same type as :None:None:`create_using`) with parallel edges. In this case, :None:None:`edge_attribute` will be ignored.

Parameters

A: scipy sparse matrix :

A biadjacency matrix representation of a graph

create_using: NetworkX graph :

Use specified graph for result. The default is Graph()

edge_attribute: string :

Name of edge attribute to store matrix numeric value. The data will have the same type as the matrix entry (int, float, (real,imag)).

Creates a new bipartite graph from a biadjacency matrix given as a SciPy sparse matrix.

See Also

biadjacency_matrix
from_numpy_array

Examples

See :

Back References

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

networkx.algorithms.bipartite.matrix.biadjacency_matrix

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/bipartite/matrix.py#120
type: <class 'function'>
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