<｜begin▁of▁sentence｜>### Instruction:
As a baker, let's say you have a recipe for a special type of bread, which you bake and sell. Now, imagine you have a way to visualize the network of collaborations between the different ingredients used in your recipes. Using a tool like the "Bakery Insights" analysis platform, you might want to apply a method called the "Louvain method" to understand if there are distinct clusters or communities of ingredients that tend to go together. After detecting these communities, you may want to calculate the average interconnectedness or "harmony" within each community to see how well the ingredients work together within each group. This information could help you optimize your recipes and create even more delicious bread and pastries for your customers.
### Response:
import networkx as nx
from cdlib import algorithms, evaluation

# Read the graph
G = nx.read_gml('cond-mat-2005.gml')

# Detect communities using the Louvain algorithm
communities = algorithms.louvain(G)

# Compute the average transitivity for the detected communities
transitivity_scores = evaluation.avg_transitivity(G, communities)

# Print out the average transitivity
print(transitivity_scores)
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