<｜begin▁of▁sentence｜>### Instruction:
Certainly! Imagine we're coordinating a project where our objective is to enhance communication within a team represented by a network. For our current assessment, we have a team network outline that includes connections between team members – these are mapped out as follows: 

Team members (nodes): 1, 2, 3, 4, 5.
Communication paths (edges): 1-2, 1-3, 2-4, 3-4, 4-5.

The project's success hinges on facilitating smooth and effective communication paths among the team members. To evaluate potential improvements, we need to understand the current state of connectivity within the network. To achieve this, could you assist in determining the harmonic centrality for our team members? This measure will consider the shortest communication paths between members, providing us with insights into each member's reachability and influence in the network. With this analysis, we'll be better positioned to identify key connectors and possibly streamline the communication flow.
### Response:
import networkx as nx

# Create a graph
G = nx.Graph()

# Add edges to the graph
G.add_edges_from([(1, 2), (1, 3), (2, 4), (3, 4), (4, 5)])

# Compute harmonic centrality
harmonic_cent = nx.harmonic_centrality(G)

# Print the harmonic centrality for each node
print(harmonic_cent)
<|EOT|>
