<｜begin▁of▁sentence｜>### Instruction:
As a security officer responsible for patrolling and monitoring properties, you might encounter a situation where you need to analyze the security of different areas within a facility. In a scenario where you are using a network of connected areas such as rooms, hallways, or checkpoints to represent the layout of a building, you might want to detect clusters of connected areas that form distinct regions within the facility.

To achieve this, you could use a community detection algorithm to identify clusters of interconnected areas within the network. After applying the algorithm, you would need to retrieve all the subgraphs belonging to each detected cluster. This would enable you to understand the interconnectedness of different areas and potentially identify areas that require additional security measures or monitoring.

As you navigate through your patrol and monitoring duties, having a clear understanding of the subgraphs belonging to each detected cluster can assist you in ensuring the overall security and safety of the facility.
### Response:
from igraph import Graph

# Create a graph with an edge list
g = Graph(edges=[(0, 1), (2, 3), (3, 4), (4, 5), (5, 6), (3, 6)])

# Community detection using a simple modularity-based algorithm
clustering = g.community_edge_betweenness()
clusters = clustering.as_clustering()

# Retrieve the subgraphs for each cluster
subgraphs = clusters.subgraphs()

# Print the number of vertices in each subgraph
for i, sg in enumerate(subgraphs):
  print(f'Subgraph {i} has {sg.vcount()} vertices')
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