<｜begin▁of▁sentence｜>### Instruction:
Imagine you're tasked with analyzing the structural integrity of a new roadway network that links various research outposts over a terrain likened to a graph structure. The connections or pathways between outposts are represented by edges, with the current map comprising the following pathways: between outpost 1 and 2, outpost 1 and 3, outpost 1 and 4, outpost 3 and 4, outpost 3 and 5, and lastly between outpost 4 and 5.

To furnish a comprehensive report on the accessibility and potential bottlenecks of this outpost network, you need to assess the eccentricity, or the maximal navigational distance, from each outpost to the furthest other in this network. Specifically, you seek to determine the eccentricity for the entire network, with a particular focus on outposts 1 and 5. 

For the sake of precision and accuracy in this exercise, employing the NetworkX library would be the appropriate methodology to compute these eccentricities, whilst ensuring the data reflects the true scope of the roadways between the outposts as detailed here. Could you recast the problem of computing the eccentricity for each outpost, and specifically for outposts 1 and 5, within the context of this scenario, utilizing NetworkX as the analytical tool?
### 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 the result
print(eccentricity)

# Compute the eccentricity of specified nodes [1, 5]
specific_eccentricity = nx.eccentricity(G, v=[1, 5])
print(specific_eccentricity)
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