<｜begin▁of▁sentence｜>### Instruction:
Imagine you're representing a tech firm that's developed a proprietary communication network, which we can visualize as a graph. In this network graph, nodes represent communication hubs and edges illustrate direct links between them. Currently, we're looking at a subset of this network, specified by the connections between hubs: [(1, 2), (1, 3), (2, 3), (2, 4), (3, 4)]. To enhance the efficiency of data transmission and identify potential points for further infrastructural development, you seek to evaluate the significance of each hub within this network.

To achieve this, we suggest conducting an analysis to determine the betweenness centrality for each hub. This measure will reflect the frequency at which a particular hub acts as a bridge along the shortest path between other hubs. Furthermore, we propose normalizing the results to account for the network size, thus providing a clearer view of each hub's relative importance within this communication framework.

Could your technical team employ NetworkX, a sophisticated tool for network analysis, to perform this evaluation and deliver the normalized metrics of betweenness centrality for each node in the network graph? This information will be instrumental in guiding your firm's strategic decisions regarding network upgrades and expansions.
### 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 betweenness centrality of the graph
betweenness = nx.betweenness_centrality(G, normalized=True)

print(betweenness)
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