<｜begin▁of▁sentence｜>### Instruction:
Imagine you've just stumbled upon a charming vintage map, detailing the relationships between different collectors and their antique items, somewhat akin to a social network of yesteryear's treasures. On this map, each collector is a point, and the lines between them represent past transactions of unique pieces. You'll find that Collector 0 has exchanged items with Collector 1, and Collector 1 has dealt with Collector 2. Both Collector 0 and Collector 1 are part of the same esteemed Antique Circle 0, while Collector 2 is aligned with the up-and-coming Antique Circle 1.

Now, let's consider a hypothetical situation. You're intrigued by the possibility of a direct transaction between Collector 0 and Collector 2, which hasn't occurred yet. To assess the potential of this connection, you're looking to apply a particular method of evaluation, known as the cn_soundarajan_hopcroft score, which accounts for their community affiliations.

Could you, with your expertise in the intricacies of relationships and value within this antiquarian network, articulate how you would calculate this score for the yet-to-occur transaction between Collector 0 and Collector 2 using the known attributes and the network's configuration? This scholarly exercise could unveil hidden potential in future dealings within this intricate tapestry of collectors and their coveted relics.
### Response:
import networkx as nx

# Create an undirected graph
G = nx.Graph()

# Add nodes along with their community attribute
G.add_node(0, community=0)
G.add_node(1, community=0)
G.add_node(2, community=1)

# Add edges
G.add_edges_from([(0, 1), (1, 2)])

# Calculate cn_soundarajan_hopcroft score
preds = nx.cn_soundarajan_hopcroft(G, [(0, 2)])

# Print the scores
for u, v, p in preds:
    print(f'({u}, {v}) -> {p}')
<|EOT|>
