<｜begin▁of▁sentence｜>### Instruction:
Imagine we are charting an advocacy network, a critical tool in our campaign for human rights, represented as a bipartite wheel graph. Our current focus group consists of five key players where actors 0, 1, and 2 make up one faction of our alliances—essentially, one set within the bipartite structure.

Suppose we wish to enhance the effectiveness of our advocacy within this network by identifying which member has the potential to most efficiently disseminate information and rally support. We would need to assess the community engagement of each individual, so to speak. In network analysis terms, this is akin to calculating the bipartite closeness centrality for each participant in our graph.

Could you assist us in determining this metric using NetworkX, thereby enabling us to strategize our outreach and mobilize our resources more effectively?
### Response:
import networkx as nx
from networkx.algorithms import bipartite

# Generate a wheel graph
G = nx.wheel_graph(5)

# Assume nodes 0, 1, 2 form one partite set, and 3, 4 the other
top_nodes = {0, 1, 2}
closeness = bipartite.closeness_centrality(G, nodes=top_nodes)

print(closeness)
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