projected_graph(B, nodes, multigraph=False)
Returns the graph G that is the projection of the bipartite graph B onto the specified nodes. They retain their attributes and are connected in G if they have a common neighbor in B.
No attempt is made to verify that the input graph B is bipartite. Returns a simple graph that is the projection of the bipartite graph B onto the set of nodes given in list nodes. If multigraph=True then a multigraph is returned with an edge for every shared neighbor.
Directed graphs are allowed as input. The output will also then be a directed graph with edges if there is a directed path between the nodes.
The graph and node properties are (shallow) copied to the projected graph.
See bipartite documentation <networkx.algorithms.bipartite>
for further details on how bipartite graphs are handled in NetworkX.
The input graph should be bipartite.
Nodes to project onto (the "bottom" nodes).
If True return a multigraph where the multiple edges represent multiple shared neighbors. They edge key in the multigraph is assigned to the label of the neighbor.
A graph that is the projection onto the given nodes.
Returns the projection of B onto one of its node sets.
>>> from networkx.algorithms import bipartite
... B = nx.path_graph(4)
... G = bipartite.projected_graph(B, [1, 3])
... list(G) [1, 3]
>>> list(G.edges()) [(1, 3)]
If nodes a, and b are connected through both nodes 1 and 2 then building a multigraph results in two edges in the projection onto [`a`, b]:
>>> B = nx.Graph()See :
... B.add_edges_from([("a", 1), ("b", 1), ("a", 2), ("b", 2)])
... G = bipartite.projected_graph(B, ["a", "b"], multigraph=True)
... print([sorted((u, v)) for u, v in G.edges()]) [['a', 'b'], ['a', 'b']]
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.projection.projected_graph
networkx.algorithms.bipartite.projection.overlap_weighted_projected_graph
networkx.algorithms.bipartite.projection.generic_weighted_projected_graph
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