<｜begin▁of▁sentence｜>### Instruction:
Consider a bipartite graph B where two distinct sets of nodes collaborate. For example, we might have authors and publications, with edges representing authorship. With bottom_nodes [0, 1, 2, 3] representing authors and the nodes labeled 'a', 'b', 'c', 'd' representing publications, how would you use Newman’s collaboration weighted projection to find the weighted graph of author collaborations, based on shared publications?
### Response:
from networkx.algorithms import bipartite
import networkx as nx

# Define a bipartite graph
B = nx.Graph()
# Add edges for bipartite graph B
B.add_edges_from([(0, 'a'), (0, 'b'), (1, 'a'), (2, 'c'), (2, 'd'), (3, 'd')])

# Specify the bottom nodes (the one set that the projection is onto)
bottom_nodes = [0, 1, 2, 3]

# Create the collaboration weighted projected graph
G = bipartite.collaboration_weighted_projected_graph(B, bottom_nodes)

# Print the edges with weights
for edge in sorted(G.edges(data=True)):
    print(edge)
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