<｜begin▁of▁sentence｜>### Instruction:
Given a directed graph with nodes {'1', '2', '3', '4', '5', '6', 'A', 'B', 'C'} and edges {('1', 'C'), ('1', 'B'), ('2', 'C'), ('2', 'B'), ('2', 'A'), ('3', 'B'), ('3', 'A'), ('3', '6'), ('4', 'C'), ('4', 'B'), ('4', 'A'), ('5', 'B'), ('5', 'A'), ('6', '5'), ('A', '6')}, how can you apply dedensification to reduce the number of edges while maintaining a graph structure that can be re-densified to reconstruct the original graph? Use a threshold of 2 for the dedensification process.
### Response:
import networkx as nx

# Create a directed graph
original_graph = nx.DiGraph()
original_graph.add_nodes_from(['1', '2', '3', '4', '5', '6', 'A', 'B', 'C'])
original_graph.add_edges_from([('1', 'C'), ('1', 'B'), ('2', 'C'), ('2', 'B'), ('2', 'A'), ('3', 'B'), ('3', 'A'), ('3', '6'), ('4', 'C'), ('4', 'B'), ('4', 'A'), ('5', 'B'), ('5', 'A'), ('6', '5'), ('A', '6')])

# Apply dedensification with a threshold of 2
c_graph, c_nodes = nx.dedensify(original_graph, threshold=2)

# Print the number of edges before and after dedensification
print('Original number of edges:', original_graph.number_of_edges())
print('Dedensified number of edges:', c_graph.number_of_edges())
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