<｜begin▁of▁sentence｜>### Instruction:
Imagine you're consulting the layout of a new cancer treatment facility; each treatment room is interconnected, akin to a linear sequence resembling a corridor with rooms positioned one after another – Room 1 connected to Room 2, Room 2 to Room 3, and so on until Room 6. For efficient patient flow and resource allocation, you need to ascertain which room is most 'central' to the layout, metaphorically measuring this in a concept not unlike closeness to all other rooms in the sequence. This will assist in determining where to station key personnel and equipment for optimal access. Utilize NetworkX – akin to a sophisticated diagnostic tool – to quantify the 'closeness centrality' of each room, thereby identifying the room that, in essence, beats at the heart of your facility's layout. How might you proceed to calculate the closeness centrality, given the arrangement of rooms described, using NetworkX?
### Response:
import networkx as nx

# Create a graph
G = nx.Graph()
G.add_edges_from([(1, 2), (2, 3), (3, 4), (4, 5), (5, 6)])

# Compute closeness centrality
centrality = nx.closeness_centrality(G)

print("Node closeness centralities:\n", centrality)
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