<｜begin▁of▁sentence｜>### Instruction:
As a curator managing a collection of historical documents in a museum, imagine you're tasked with analyzing the interconnectedness of articles referencing historical events. You have a dataset representing citations among articles, where each edge (a, b) denotes that article a cites article b. For instance, articles could discuss various aspects of World War II, with citations linking them based on shared references.

Now, you're exploring how to use a specialized tool, like the `cocitation` function in the `igraph` library, to compute the cocitation score for pairs of articles. This score helps measure the degree of similarity or relatedness between articles based on shared citations. By understanding the cocitation patterns, you can gain insights into the thematic connections within your historical document collection and how articles intersect in their referencing of primary sources.
### Response:
import igraph as ig

# Create a simple undirected graph with edges representing citations
edges = [(0, 1), (0, 2), (1, 2), (3, 2), (3, 4), (4, 2), (4, 5), (5, 3)]
g = ig.Graph(edges=edges)

# Calculate the cocitation for vertices 2 and 3, which are both cited by vertex 4
cocitation_score = g.cocitation([2, 3])

# Print the cocitation score
print(f'Cocitation score between vertices 2 and 3 is: {cocitation_score}')
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