<｜begin▁of▁sentence｜>### Instruction:
Given a bipartite graph with the edges set [('A', '1'), ('A', '2'), ('B', '1'), ('B', '3'), ('C', '2'), ('C', '3')] and knowing that nodes '1', '2', and '3' are part of one set of the bipartite graph, how can I compute the closeness centrality of nodes in the network?
### Response:
import networkx as nx
from networkx.algorithms import bipartite

# Create a bipartite graph
B = nx.Graph()
# Add edges between nodes of two partition sets
B.add_edges_from([('A', '1'), ('A', '2'), ('B', '1'), ('B', '3'), ('C', '2'), ('C', '3')])
# Define one set of the bipartite graph
top_nodes = set(['1', '2', '3'])
# Assuming that 'top_nodes' contains all nodes from one part of the bipartite graph
# Calculate closeness centrality
closeness = bipartite.closeness_centrality(B, nodes=top_nodes)
print(closeness)
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