<｜begin▁of▁sentence｜>### Instruction:
As a security officer responsible for monitoring and safeguarding properties, facilities, or areas, you're often tasked with maintaining security protocols and ensuring the safety of people and assets within your jurisdiction. One of your duties may involve analyzing potential threats and identifying patterns of behavior that could indicate suspicious activity.

Imagine you're stationed at a corporate office building equipped with sophisticated surveillance systems to monitor the premises. Your supervisor has provided you with access to the building's network, which includes a file named 'graph_data.gml'. This file contains data representing the interconnectedness of various areas within the building, including corridors, rooms, and entry points.

Now, your task is to leverage the capabilities of a specialized tool called community_edge_betweenness from the igraph library to analyze the community structure within the building. By applying this tool to the graph data, you aim to detect any distinct clusters or communities of interconnected areas.

Once you've executed the community detection algorithm, your objective is to print out the membership of each vertex, effectively identifying which areas belong to which community within the building. This information could prove invaluable in understanding the flow of movement within the premises and identifying any potential vulnerabilities or areas requiring additional security measures.
### Response:
import igraph as ig

# Read graph from file
g = ig.Graph.Read_GML('graph_data.gml')

# Perform community detection based on edge betweenness
community_dendrogram = g.community_edge_betweenness(directed=False, weights=None)

# Convert the dendrogram into a clustering at the optimal cut
optimal_clustering = community_dendrogram.as_clustering()

# Print the membership of each vertex
print(optimal_clustering.membership)
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