<｜begin▁of▁sentence｜>### Instruction:
As a landscape architect who plans and designs outdoor spaces, you may need to analyze the connectivity and relationship between different elements within a landscape. Imagine you are working on the design of a public park that consists of two distinct areas, each with its own unique features. One area includes a playground, a seating area, and a small garden, while the other area features a walking trail, a fountain, and a picnic area.

In order to evaluate the organization and clustering of these elements, you could represent the relationships between them as a graph with vertices representing different features of the park. Now considering two different ways of categorizing the features into clusters, you may want to compare these clusterings using the variation of information metric to understand how the organization of the park elements differs in terms of functionality, design, and visitor experience.
### Response:
from igraph import Graph, Clustering

# Create a graph with 6 vertices and some edges
graph = Graph(edges=[(0, 1), (0, 2), (1, 2), (3, 4), (4, 5), (3, 5)])

# Define two distinct clusterings of the vertices
clustering1 = Clustering([0, 0, 0, 1, 1, 1])
clustering2 = Clustering([0, 0, 1, 1, 1, 0])

# Compare clusterings using the 'vi' metric for variation of information
similarity = clustering1.compare_to(clustering2, method='vi')
print('Similarity Measure (Variation of Information):', similarity)
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