<｜begin▁of▁sentence｜>### Instruction:
In the context of optimizing the interconnected pathways and intersections of academic collaborations much like a city's traffic network, let's envision a scenario where we're analyzing the collaborative infrastructure between authors and research studies. In this scenario, consider authors as key traffic nodes, labeled 0, 1, 2, akin to major intersections in a city. The research studies are akin to streets, labeled 3, 4, 5, connecting these nodes. The traffic flows in this academic network are represented by the authorship connections, for instance, node 0 (Author 0) is connected to streets 3 and 4 (Papers 3 and 4), while node 1 (Author 1) is also connected to streets 3 and 4, and node 2 (Author 2) to streets 4 and 5.

Our task is akin to designing a model that allows us to observe and quantify the strength of the traffic flow, or in this case, the collaboration strength between authors. Utilizing Newman's model of collaboration within this network, similar to a traffic flow model, how might we create a 'projection' onto our core intersections — the authors — whereby the weight of each connecting road signifies the robustness of their collaboration efforts? 

This projection would enable us to see directly how authors are connected to each other, without the intermediate 'streets' of papers, helping understand the collaboration network's structure and identify key areas for potential infrastructure improvement.
### Response:
import networkx as nx
from networkx.algorithms import bipartite

# Create a bipartite graph
B = nx.Graph()
# Add edges between top and bottom nodes
B.add_edges_from([(0, 3), (0, 4), (1, 3), (1, 4), (2, 4), (2, 5)])

# Define the bottom nodes - the ones onto which we will project
bottom_nodes = {0, 1, 2}

# Project the bipartite graph onto bottom nodes using the collaboration weighted projection
projected_graph = bipartite.collaboration_weighted_projected_graph(B, bottom_nodes)

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