<｜begin▁of▁sentence｜>### Instruction:
As a sales associate working in a bustling department store, you're often faced with the challenge of understanding how well two different product displays or sections complement each other. Imagine you've been tasked with comparing two arrangements of products within your store to determine how similar or dissimilar they are.

To accomplish this, you'd utilize a concept similar to what networkx calls "split-join distance." This involves assessing the degree of overlap and divergence between the two arrangements. In simpler terms, you're essentially measuring how much the products in one display match or differ from those in another.

For instance, if one arrangement includes mostly clothing items while the other focuses on accessories, there's a clear split between the two. Conversely, if both displays feature a mix of clothing, shoes, and accessories, with only slight variations in the product types or brands, they're likely more similar.

By understanding this split-join distance, you can make informed decisions about rearranging products to enhance overall sales performance or to ensure a cohesive shopping experience for customers. Just as you assist customers in finding the perfect product match, this approach helps you ensure the perfect match between different product arrangements within your store.
### Response:
from igraph import Graph, Clustering

# Assuming we have two community structures defined
comm1 = Clustering([0, 0, 1, 1, 0, 1])
comm2 = Clustering([0, 0, 0, 1, 1, 1])

# Calculate the split-join distance
split_join_dist = Graph.split_join_distance(comm1, comm2)

# Print the split-join distance
print('Split-join distance:', sum(split_join_dist))
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