<｜begin▁of▁sentence｜>### Instruction:
Imagine you're crafting the user interface for a social network analysis tool, and you're currently working on a feature that visualizes the connections and interactions between different users in an online community. The data for the community interactions is structured similarly to a graph with connections representing interactions between users.

Within this digital landscape, you've been presented with a specific set of interaction pairs, akin to a graph's edge set: [('A', 'B'), ('A', 'C'), ('B', 'C'), ('C', 'D'), ('D', 'E'), ('E', 'F'), ('E', 'G')]. The task at hand is to create a visual module that not only shows these connections but also incorporates a metric known as the "dispersion score" – a numerical value representing the strength of a tie based on mutual connections – which will enhance the users' understanding of the depth of relationships in their network.

Your UI design challenge is to ideate how you will display a measurement of the dispersion score between the user represented by node 'A' and all other users within this set of connections. This feature would empower users to discern which relationships might be worth investing in more deeply based on the strength of their shared contacts.

How might you envision this design within the interface, and what would be the most effective way to communicate the concept of dispersion scores to the user effectively while maintaining aesthetics and usability?
### Response:
import networkx as nx

# Create a graph
G = nx.Graph()
G.add_edges_from([('A', 'B'), ('A', 'C'), ('B', 'C'), ('C', 'D'), ('D', 'E'), ('E', 'F'), ('E', 'G')])

# Calculate dispersion between node 'A' and all other nodes
dispersion_scores = nx.dispersion(G, u='A', normalized=True)

print(dispersion_scores)
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