<｜begin▁of▁sentence｜>### Instruction:
As a construction manager tasked with overseeing a project's organizational structure, imagine we're blueprinting a network that illustrates the workflow between two distinct teams: Team A (supervisors) and Team B (workers). Now, each supervisor in Team A has a specific number of connections representing their direct reports or the workers they manage. Initially, we have four supervisors with desired connections as follows: Supervisor 1 needs to manage 3 workers, Supervisor 2 oversees 2 workers, and both Supervisor 3 and Supervisor 4 are in charge of just 1 worker each.

As our workforce (Team B) expands, we employ a dynamic assignment model where with every new worker added there's a 5% chance that they will be hired under Team B. When adding these new workers, we tend to assign them preferentially to those supervisors who already oversee a larger number of workers – essentially following the 'the rich get richer' principle of network growth.

We need to construct this hierarchy in the form of a bipartite graph. Could you arrange to depict this evolving structure? We want a visual map that clearly identifies each member of Team A and Team B, along with a comprehensive list of which workers report to which supervisors. This clear representation will be crucial for us to manage resources efficiently and ensure that the workload is distributed effectively across the teams as our project progresses.
### Response:
import networkx as nx
from networkx.algorithms import bipartite

# Degree sequence for node set A
aseq = [3, 2, 1, 1]

# Probability that a new bottom node is added each time
p = 0.05

# Create a bipartite preferential attachment graph
B = bipartite.preferential_attachment_graph(aseq, p, seed=42)

# Get the nodes from set A and B
A_nodes = {n for n, d in B.nodes(data=True) if d['bipartite'] == 0}
B_nodes = set(B) - A_nodes

# Display the sets of nodes and the edges
print('Nodes in set A:', A_nodes)
print('Nodes in set B:', B_nodes)
print('Edges:', list(B.edges()))
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