<｜begin▁of▁sentence｜>### Instruction:
Imagine we're about to launch a new social networking platform and we're analyzing the interconnectedness of our initial user base to optimize our recommendation engine. We have a subset of users, and we've mapped out their direct connections, somewhat similar to how one might draft a user interaction roadmap.

The current connections are as follows: User 1 is connected to Users 2 and 3; User 2 is connected to Users 1, 3, and 4; User 3 is connected to Users 1, 2, and 4; and finally, User 4 is connected to Users 2 and 3.

As the product manager, I'd like you to utilize NetworkX, a robust tool we often employ for such network analysis, to evaluate and deliver insights into the closeness centrality for each of these users. This will essentially reveal which user is most central within our network, based on their direct and indirect connections, thereby allowing us to understand our platform's initial social structure and possibly identify key early adopters. Could you proceed with this analysis and prepare a brief report on your findings?
### Response:
import networkx as nx

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

# Compute the closeness centrality for the nodes
centrality = nx.closeness_centrality(G)

print('Closeness centrality:', centrality)
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