<｜begin▁of▁sentence｜>### Instruction:
As an ergonomist, you're tasked with optimizing the design of office spaces to ensure maximum comfort and efficiency for employees. Imagine you're working on a project for a tech company that wants to enhance collaboration among its teams by restructuring its office layout. To achieve this, you're exploring network analysis tools to understand how different departments interact within the company.

You have access to data representing the connections between various departments, visualized as a graph. Now, you're considering employing the `community_leading_eigenvector` function provided by the igraph API. This function utilizes Newman's leading eigenvector method to detect community structures within the graph, helping you identify clusters of departments that frequently collaborate and interact with each other.

Your objective is to leverage this analysis to inform the company's office redesign, strategically placing departments with strong connections closer together to foster better communication and teamwork, ultimately optimizing the office layout for enhanced productivity and collaboration.
### Response:
from igraph import Graph

# Load your graph (assuming it is loaded here as variable 'g')
# For example, a small network
edges = [(0, 1), (0, 2), (1, 2), (3, 4), (3, 5), (4, 5)]
g = Graph(edges=edges)

# Execute Newman's leading eigenvector method
clustering = g.community_leading_eigenvector()

# View the result
print(clustering)
print(clustering.membership)
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