<｜begin▁of▁sentence｜>### Instruction:
As an assistant familiar with networkx, I can help you compare the community structures of two graphs using the variation of information metric. Imagine you are running an organic farm and have two separate fields with different layouts and arrangements of crops. Each field represents a graph with its unique community structure. Now, you want to assess the similarity or difference between the community arrangements in the two fields to understand how cohesive they are and later optimize the crop rotations and harvesting schedules.
### Response:
import igraph as ig

# Load two graphs with community structures
graph1 = ig.Graph.Famous('Zachary')
graph2 = ig.Graph.Famous('Zachary')

# Assume we have vertex clustering for both graphs
c1 = ig.VertexClustering(graph1)
c2 = ig.VertexClustering(graph2)

# Compare the two clusterings
similarity = c1.compare_to(c2, method='vi')
print('Similarity (variation of information):', similarity)
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