<｜begin▁of▁sentence｜>### Instruction:
Imagine we're examining the health and interconnectedness of a community, which we can represent through the "karate club graph" – a common illustration in social dynamics. In this scenario, we're particularly interested in a subset of the community, let's refer to them as patients S, which includes individuals 1, 2, and 3. For a robust community diagnosis, we aspire to ascertain the collective closeness of these patients to the rest of the community members.

This 'group closeness centrality' is akin to gauging how efficiently this subgroup can access the entire network's resources or how swiftly information can spread from this group to all other individuals in the community. Using this metaphor, can we determine the group closeness centrality for our patients S within the relationship network modeled by the karate club graph, ensuring we have a clear picture of the group's proximity and potential influence on the larger social body?
### Response:
import networkx as nx

# Create a graph
G = nx.karate_club_graph()

# Define a group of nodes for which we want to find group closeness centrality
S = [1, 2, 3]

# Compute the group closeness centrality
closeness = nx.group_closeness_centrality(G, S)

print(f'Group closeness centrality for nodes {S}: {closeness}')
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