Bipartite graph in NetworkX

Question

B.add_nodes_from(a, bipartite=1)
B.add_nodes_from(b, bipartite=0)
nx.draw(B, with_labels = True)  
plt.savefig("graph.png")

I am getting the following figure. How can I make it look like a proper bipartite graph?

Answer 1

NetworkX already has a function to do exactly this.

Its called networkx.drawing.layout.bipartite_layout

You use it to generate the dictionary that is fed to the drawing functions like nx.draw via the pos argument like so:

nx.draw_networkx(
    B,
    pos = nx.drawing.layout.bipartite_layout(B, B_first_partition_nodes), 
    width = edge_widths*5) # Or whatever other display options you like

Where B is the full bipartite graph (represented as a regular networkx graph), and B_first_partition_nodes are the nodes you wish to place in the first partition.

This generates a dictionary of numeric positions that is passed to the pos argument of the drawing function. You can specify layout options as well, see the main page.

Obligatory example output:

Answer 2

Another example, combining graph with bipartite graph:

G = nx.read_edgelist('file.txt', delimiter="\t")
aux = G.edges(data=True)
B = nx.Graph()
B.add_nodes_from(list(employees), bipartite=0)
B.add_nodes_from(list(movies), bipartite=1)
B.add_edges_from(aux)

%matplotlib notebook
import [matplotlib][1].pyplot as plt
plt.figure()

edges = B.edges()
print(edges)
X, Y = bipartite.sets(B)
pos = dict()
pos.update( (n, (1, i)) for i, n in enumerate(X) ) # put nodes from X at x=1
pos.update( (n, (2, i)) for i, n in enumerate(Y) ) # put nodes from Y at x=2
nx.draw_networkx(B, pos=pos, edges=edges)
plt.show()

Answer 3

Inspired in mdml's answer, another way to plot a complete bipartite graph in a "classical" way:

import networkx as nx
import matplotlib.pyplot as plt

m, n = 5, 10
K = nx.complete_bipartite_graph(m, n)
pos = {}
pos.update((i, (i - m/2, 1)) for i in range(m))
pos.update((i, (i - m - n/2, 0)) for i in range(m, m + n))

fig, ax = plt.subplots()
fig.set_size_inches(15, 4)
nx.draw(K, with_labels=True, pos=pos, node_size=300, width=0.4)
plt.show()