configuration_model(aseq, bseq, create_using=None, seed=None)
The sum of the two sequences must be equal: sum(aseq)=sum(bseq) If no graph type is specified use MultiGraph with parallel edges. If you want a graph with no parallel edges use create_using=Graph() but then the resulting degree sequences might not be exact.
The nodes are assigned the attribute 'bipartite' with the value 0 or 1 to indicate which bipartite set the node belongs to.
This function is not imported in the main namespace. To use it use nx.bipartite.configuration_model
Degree sequence for node set A.
Degree sequence for node set B.
Return graph of this type.
Indicator of random number generation state. See Randomness<randomness>
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Returns a random bipartite graph from two given degree sequences.
The following pages refer to to this document either explicitly or contain code examples using this.
networkx.algorithms.bipartite.generators.random_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