<｜begin▁of▁sentence｜>### Instruction:
Certainly. Imagine we are examining a policy network where agencies and policy actions are represented as two distinct groups within our model. We wish to simulate the potential interactions between 5 agencies (group one) and 10 policy actions (group two) to anticipate how frequently engagement might occur. To do this, we'll employ a probabilistic model to generate hypothetical connections, assigning a 30% chance that any given agency will be involved with a particular policy action.

As a policy analyst, you're tasked with constructing an undirected bipartite random graph as a simplified model of this network. The graph must incorporate the specified number of agencies and policy actions, alongside the established probability for the formation of connections. Once the model is generated, the next step is to distinguish between the two categories of nodes—those representing agencies, and those representing policy actions—to analyze the architecture of this network.

By simulating and scrutinizing this network, you can begin to understand the potential complexity of interactions and inform the development of more efficient and targeted policy strategies.
### Response:
import networkx as nx
from networkx.algorithms import bipartite
B = bipartite.random_graph(n=5, m=10, p=0.3, seed=42, directed=False)
top_nodes = {n for n, d in B.nodes(data=True) if d['bipartite']==0}
bottom_nodes = set(B) - top_nodes
print('Top nodes:', top_nodes)
print('Bottom nodes:', bottom_nodes)
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