<｜begin▁of▁sentence｜>### Instruction:
Imagine you're tasked with revitalizing a community park, intending to create a series of interconnected spaces that promote both leisure and social interaction. To achieve this, you've conceptualized the park's layout as a sequence of pavilions, green spaces, and walkways, akin to the structure of a cubical framework – a model you've represented using a landscape design software that employs a `nx.cubical_graph()` structure to map out the interconnected elements of your design.

Within this virtual model of your park, consider a crucial subset of spaces - let's refer to them simply as pavilions numbered 0, 1, 2, and 3 - which are gathered in the heart of the park. As a landscape architect, you understand the importance of flow between these areas, not only to each other but to the surrounding environment of the park as well.

Now, to ensure that these pavilions are effectively integrated into the broader park layout and are not isolated, you need to assess the 'node expansion' of this subset within your design model. This means examining how these four pavilions (nodes) are connected to adjacent spaces (nodes) outside of this central subset, which, in landscape design terms, translates to conceptualizing the potential pathways and sightlines that expand from these pivotal pavilions to other areas of the park.

How would you then describe the measure of connectivity or the 'node expansion' for these pavilions within the virtual model of your park design? This evaluation will guide you in ensuring that the central spaces of the park are neither too secluded nor excessively open, maintaining a balance between tranquility and connectivity.
### Response:
import networkx as nx

# Create a graph
G = nx.cubical_graph()

# Define a set S of nodes to find the node expansion of
S = {0, 1, 2, 3}

# Calculate the node expansion
expansion = nx.node_expansion(G, S)

print('Node Expansion:', expansion)
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