<｜begin▁of▁sentence｜>### Instruction:
As a patent examiner, your task is to assess the uniqueness and patentability of submitted inventions. Imagine you've received a set of blueprints detailing a new type of transportation system. Your job is to analyze the design to determine its novelty and potential for patent approval. However, the blueprints are complex, resembling a network of interconnected components. To make sense of this intricate design, you need to utilize specialized software called "igraph." With this tool, you can apply Newman's leading eigenvector method to identify distinct clusters within the transportation system. Each cluster represents a community of closely related components. Your objective is to use igraph to detect these communities and document which components belong to each one. This analysis will provide valuable insight into the structure and functionality of the proposed transportation system, helping you make an informed decision regarding its patentability.
### Response:
import igraph as ig

# Read graph from GML file
graph = ig.read('network.gml')

# Apply Newman's leading eigenvector method
clustering = graph.community_leading_eigenvector()

# Print the membership of each vertex to a community
print(clustering.membership)
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