<｜begin▁of▁sentence｜>### Instruction:
As an industrial designer who specializes in developing mass-produced products, you have been tasked with designing and analyzing a complex set of interconnected product features for a new line of smart home devices. The data for these features is stored in a file named 'network.gml', which represents the relationships and dependencies between the various features. Your objective is to apply community detection using the Louvain method to identify distinct groups of features that have strong interactions and dependencies, allowing for more focused development and design efforts. Additionally, you need to retrieve the subgraphs for each detected community to further analyze the relationships and potential synergies among the different feature groups.
### Response:
from igraph import Graph
import igraph as ig

# Load a graph from file
graph = ig.Graph.Read_GML('network.gml')

# Perform a community detection algorithm, for example the Louvain method
communities = graph.community_multilevel()

# Get all the subgraphs corresponding to each cluster
subgraphs = communities.subgraphs()

# Optionally, you can print the summary of each subgraph
for i, sg in enumerate(subgraphs):
    print(f'Subgraph {i}:', sg.summary())
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