<｜begin▁of▁sentence｜>### Instruction:
As a humanitarian worker in charge of managing relief efforts in a region impacted by a natural disaster, you have access to a database containing information about the affected population and their connections within the community. This data is stored in a file named 'network.gml', and it includes potential strength of relationships between individuals. To better understand and identify cohesive groups within the affected population, you would like to utilize the edge betweenness community detection algorithm. Can you explain how I can implement this algorithm to detect communities in the network and ultimately support our relief efforts more effectively?
### Response:
import igraph as ig

# Assume 'graph' is an igraph.Graph object, possibly with edge weights
# Load graph from a file (this file should contain your graph structure)
graph = ig.Graph.Read_GML('network.gml')

# Execute the community detection algorithm based on edge betweenness
# 'weights' could be the name of an edge attribute or a list of edge weights
communities = graph.community_edge_betweenness(directed=False, weights='weight')

# Convert the resulting dendrogram into a flat clustering
clusters = communities.as_clustering()

# Print the clusters
print(clusters)

# Save the clustering (optional)
clusters.save('clusters.txt')
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