<｜begin▁of▁sentence｜>### Instruction:
As a data scientist, you're tasked with analyzing a complex network that models numerous interactions or pathways between various entities, represented by a directed graph. Your graph includes nodes labeled ['1', '2', '3', '4', '5', '6', 'A', 'B', 'C'] and contains pathways signified by the following 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')].

In an effort to streamline this complex network for more efficient analysis without compromising the underlying structure, you are considering the application of a dedensification strategy using NetworkX. Can you strategize how to employ the `dedensify` function to this network graph to reduce the number of edges, particularly around nodes with high connectivity?

The goal is to create an optimized version of the network, where the total number of connections is minimized while the original graph’s topology is largely preserved. To evaluate the effectiveness of your dedensification, compare the original number of edges to the reduced count post-dedensification, and identify any new 'compressor' nodes introduced by the process that serve to condense pathways in the graph. How would you proceed with this optimization, and what would be the key metrics to report on the outcome?
### 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')
])

c_graph, c_nodes = nx.dedensify(original_graph, threshold=2)

print('Original graph number of edges:', original_graph.number_of_edges())
print('Dedensified graph number of edges:', c_graph.number_of_edges())
print('Compressor nodes:', c_nodes)
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