<｜begin▁of▁sentence｜>### Instruction:
Certainly! Imagine you are a concierge at a bustling luxury hotel, catering to the needs of various esteemed guests. Each guest frequents different amenities within the hotel, like the lounge 'A', the restaurant 'B', the spa 'C', the fitness center 'D', and the pool 'E'. The paths between these amenities are well-trafficked corridors, represented by the connections: 'A' to 'B', 'B' to 'C', 'C' to 'D', 'D' to 'E', 'E' to 'A', 'A' to 'C', and 'B' to 'D'.

The hotel management is aiming to improve guest experience by analyzing the flow of foot traffic through these corridors. To assist with this, they have asked you to calculate a metric known as the "betweenness centrality" for each corridor. This metric will help identify which corridors are most frequently used as routes by the guests, essentially acting as bridges that connect various points of interest within the hotel. Could you provide the management with this insightful analysis utilizing NetworkX, a tool akin to the hotel's navigational blueprint, to highlight the corridors that are the key connectors based on their betweenness centrality scores?
### Response:
import networkx as nx

# Create a graph
G = nx.Graph()
G.add_edges_from([('A', 'B'), ('B', 'C'), ('C', 'D'), ('D', 'E'), ('E', 'A'), ('A', 'C'), ('B', 'D')])

# Compute edge betweenness centrality
edge_bet_centrality = nx.edge_betweenness_centrality(G, normalized=True)

print(edge_bet_centrality)
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