<｜begin▁of▁sentence｜>### Instruction:
Given a graph with nodes assigned to different communities and edges that connect nodes within the same community, how can the common neighborhood Soundarajan-Hopcroft score be computed for pairs of nodes from different communities, considering that this score is higher if a pair of nodes share neighbors that are in the same community?
### Response:
import networkx as nx

# Create a graph
G = nx.Graph()

# Add nodes with community attribute
G.add_node(0, community=0)
G.add_node(1, community=0)
G.add_node(2, community=1)
G.add_node(3, community=1)

# Add edges
G.add_edge(0, 1)
G.add_edge(2, 3)

# Get common neighborhood Soundarajan-Hopcroft scores
preds = nx.cn_soundarajan_hopcroft(G, [(1, 2), (0, 3)])

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