<｜begin▁of▁sentence｜>### Instruction:
In the dynamic landscape of brand networking, imagine we're mapping out the connections between various stakeholders within a brand's ecosystem. Picture a scenario where relationships are represented by linkages connecting individuals such as brand ambassadors (1), social media influencers (2, 3, and 4), and content creators (5). In this scenario, we have identified specific interactions: Ambassador 1 has forged connections with Influencers 2, 3, and 4; Influencer 3 has also engaged with 4 and Creator 5; and a collaboration exists between Influencer 4 and Creator 5.

In order to strategize the optimal reach for our brand message, could you employ NetworkX to determine the 'reachability' factor or, in technical terms, the eccentricity of each of these individuals within our network graph? By calculating this, we'll be able to assess who has the strongest potential to spread our brand’s influence through the network.
### Response:
import networkx as nx

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

# Compute the eccentricity of each node
eccentricity = nx.eccentricity(G)

print(eccentricity)
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