<｜begin▁of▁sentence｜>### Instruction:
As a Wine Sommelier, imagine you curated a selection of wines for a tasting event at a local winery. Each bottle of wine represents an article, and the relationships between the wines based on their flavors and characteristics can be represented as a graph. This graph would have nodes for each bottle of wine and edges representing similarities or connections between them. Now, you want to calculate the "pairing score" for a subset of the wines to determine how closely they are linked in terms of their flavors and characteristics. This score will help you advise customers on which wines may complement each other well during the tasting event.
### Response:
from igraph import Graph

# Creating a graph with 5 nodes
# This is just an example, in practice, the edges should represent citations
n = 5
g = Graph(n)
g.add_edges([(0, 1), (0, 2), (1, 3), (2, 3), (3, 4)])

vertex_ids = [1, 2, 3] # Vertices for which we want to calculate bibliographic coupling scores
scores = g.bibcoupling(vertices=vertex_ids)

print(scores)
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