is_bipartite_node_set(G, nodes)
An exception is raised if the input nodes are not distinct, because in this case some bipartite algorithms will yield incorrect results. For connected graphs the bipartite sets are unique. This function handles disconnected graphs.
Check if nodes are a one of a bipartite set.
Returns True if nodes and G/nodes are a bipartition of G.
>>> from networkx.algorithms import bipartiteSee :
... G = nx.path_graph(4)
... X = set([1, 3])
... bipartite.is_bipartite_node_set(G, X) True
The following pages refer to to this document either explicitly or contain code examples using this.
networkx.algorithms.bipartite.projection.collaboration_weighted_projected_graph
networkx.algorithms.bipartite.basic.is_bipartite_node_set
networkx.algorithms.bipartite.projection.overlap_weighted_projected_graph
networkx.algorithms.bipartite.projection.generic_weighted_projected_graph
networkx.algorithms.bipartite.projection.projected_graph
networkx.algorithms.bipartite.basic.is_bipartite
networkx.algorithms.bipartite.projection.weighted_projected_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